Overview

Dataset statistics

Number of variables81
Number of observations78497
Missing cells3445611
Missing cells (%)54.2%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory48.5 MiB
Average record size in memory648.0 B

Variable types

Numeric23
Text7
Categorical40
Unsupported11

Alerts

6-medio_o_forma_por_el_cual_conoció_la_universidad has constant value ""Constant
12-número_del_colegio has constant value ""Constant
29-no has constant value ""Constant
29-si has constant value ""Constant
30-abuelas/os has constant value ""Constant
30-amigas/os has constant value ""Constant
30-esposa/o_o_pareja has constant value ""Constant
30-hermanas/os has constant value ""Constant
30-hijas/os has constant value ""Constant
30-madre has constant value ""Constant
30-padre has constant value ""Constant
30-sobrinas/os has constant value ""Constant
30-vivo_sola/o has constant value ""Constant
Dataset has 5 (< 0.1%) duplicate rowsDuplicates
documento is highly overall correlated with 4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar? and 26 other fieldsHigh correlation
3-minutos is highly overall correlated with 36-francés and 1 other fieldsHigh correlation
5-me_la_recomendaron is highly overall correlated with 10-¿El Colegio es… (Privado/Público) and 16 other fieldsHigh correlation
5-no_se_toma_examen_de_ingreso is highly overall correlated with 10-¿El Colegio es… (Privado/Público) and 18 other fieldsHigh correlation
5-por_el_prestigio_de_la_carrera is highly overall correlated with 5-por_su_calidad_académicaHigh correlation
5-por_la_cercanía is highly overall correlated with 8-¿Cuál fue el nivel mas alto que terminaste? and 3 other fieldsHigh correlation
5-por_su_calidad_académica is highly overall correlated with 5-por_el_prestigio_de_la_carrera and 13 other fieldsHigh correlation
5-se_dicta_la_carrera_que_prefiero is highly overall correlated with 7-¿Qué nivel estás cursando? and 17 other fieldsHigh correlation
32-medio_o_polimodal is highly overall correlated with 10-¿El Colegio es… (Privado/Público) and 12 other fieldsHigh correlation
32-primario_o_egb is highly overall correlated with 36-francés and 3 other fieldsHigh correlation
32-terciario_no_universitario is highly overall correlated with 32-universitariosHigh correlation
32-universitarios is highly overall correlated with 32-terciario_no_universitarioHigh correlation
33-no_trabajan_pero_estan_buscando_trabajo is highly overall correlated with 10-¿El Colegio es… (Privado/Público) and 1 other fieldsHigh correlation
4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar? is highly overall correlated with documentoHigh correlation
7-¿Qué nivel estás cursando? is highly overall correlated with documento and 1 other fieldsHigh correlation
8-¿Cuál fue el nivel mas alto que terminaste? is highly overall correlated with documento and 1 other fieldsHigh correlation
9-¿Terminaste o estás cursando tus estudios en un Colegio? is highly overall correlated with documentoHigh correlation
10-¿El Colegio es… (Privado/Público) is highly overall correlated with documento and 6 other fieldsHigh correlation
11-Número del Colegio is highly overall correlated with documento and 3 other fieldsHigh correlation
17-¿Tenés Obra Social y/o Mutual? is highly overall correlated with documentoHigh correlation
18-¿Trabajás actualmente? is highly overall correlated with documento and 1 other fieldsHigh correlation
19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es: is highly overall correlated with documento and 2 other fieldsHigh correlation
20-En tu trabajo principal sos... is highly overall correlated with documento and 7 other fieldsHigh correlation
21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal? is highly overall correlated with documento and 5 other fieldsHigh correlation
23-¿Tu trabajo principal es? is highly overall correlated with documento and 5 other fieldsHigh correlation
24-En una semana normal de trabajo ¿cuántas horas trabajás? is highly overall correlated with documento and 5 other fieldsHigh correlation
25-Tus horarios son… is highly overall correlated with documento and 5 other fieldsHigh correlation
26-¿Qué momentos del día abarca tu jornada de trabajo? is highly overall correlated with documento and 5 other fieldsHigh correlation
27-¿Podés cambiar los horarios de trabajo? is highly overall correlated with documento and 5 other fieldsHigh correlation
28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan) is highly overall correlated with documento and 4 other fieldsHigh correlation
34-madre is highly overall correlated with documento and 1 other fieldsHigh correlation
34-¿Tenés conocimientos de otros idiomas? is highly overall correlated with documento and 2 other fieldsHigh correlation
36-francés is highly overall correlated with documento and 10 other fieldsHigh correlation
36-inglés is highly overall correlated with documento and 3 other fieldsHigh correlation
36-otros is highly overall correlated with documento and 12 other fieldsHigh correlation
36-portugués is highly overall correlated with documento and 7 other fieldsHigh correlation
37-tu_casa is highly overall correlated with documento and 2 other fieldsHigh correlation
37-tu_trabajo is highly overall correlated with documento and 7 other fieldsHigh correlation
38-planilla_de_cálculo is highly overall correlated with documento and 3 other fieldsHigh correlation
38-procesador_de_texto is highly overall correlated with documento and 2 other fieldsHigh correlation
11-Número del Colegio is highly imbalanced (62.0%)Imbalance
36-otros is highly imbalanced (66.6%)Imbalance
carrera has 7128 (9.1%) missing valuesMissing
sede has 7128 (9.1%) missing valuesMissing
documento has 7128 (9.1%) missing valuesMissing
1-¿Con quien vas a vivir durante el periodo de clases? has 12229 (15.6%) missing valuesMissing
2-¿Por qué medio te trasladarás hasta la sede donde va a cursar durante el periodo de clases? has 12270 (15.6%) missing valuesMissing
3-minutos has 16128 (20.5%) missing valuesMissing
4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar? has 12672 (16.1%) missing valuesMissing
5-es_gratuita has 29096 (37.1%) missing valuesMissing
5-me_la_recomendaron has 43480 (55.4%) missing valuesMissing
5-no_se_toma_examen_de_ingreso has 54773 (69.8%) missing valuesMissing
5-otras_razones has 66513 (84.7%) missing valuesMissing
5-por_el_prestigio_de_la_carrera has 52349 (66.7%) missing valuesMissing
5-por_la_cercanía has 34384 (43.8%) missing valuesMissing
5-por_su_calidad_académica has 41510 (52.9%) missing valuesMissing
5-se_dicta_la_carrera_que_prefiero has 27330 (34.8%) missing valuesMissing
6-medio_o_forma_por_el_cual_conoció_la_universidad has 77663 (98.9%) missing valuesMissing
7-¿Qué nivel estás cursando? has 13115 (16.7%) missing valuesMissing
8-¿Cuál fue el nivel mas alto que terminaste? has 54729 (69.7%) missing valuesMissing
9-¿Terminaste o estás cursando tus estudios en un Colegio? has 14988 (19.1%) missing valuesMissing
10-¿El Colegio es… (Privado/Público) has 55365 (70.5%) missing valuesMissing
11-Número del Colegio has 24198 (30.8%) missing valuesMissing
12-número_del_colegio has 73433 (93.5%) missing valuesMissing
13-nombre_del_colegio has 17181 (21.9%) missing valuesMissing
14-partido/depto has 16982 (21.6%) missing valuesMissing
14-país has 15279 (19.5%) missing valuesMissing
14-provincia has 15397 (19.6%) missing valuesMissing
15-Orientación del título de Nivel Medio o Polimodal has 15354 (19.6%) missing valuesMissing
16-descripción has 19300 (24.6%) missing valuesMissing
17-¿Tenés Obra Social y/o Mutual? has 14437 (18.4%) missing valuesMissing
18-¿Trabajás actualmente? has 13935 (17.8%) missing valuesMissing
19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es: has 14347 (18.3%) missing valuesMissing
20-En tu trabajo principal sos... has 48424 (61.7%) missing valuesMissing
21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal? has 49632 (63.2%) missing valuesMissing
22-¿Tu trabajo principal es? has 49649 (63.2%) missing valuesMissing
23-¿Tu trabajo principal es? has 48965 (62.4%) missing valuesMissing
24-En una semana normal de trabajo ¿cuántas horas trabajás? has 49265 (62.8%) missing valuesMissing
25-Tus horarios son… has 48540 (61.8%) missing valuesMissing
26-¿Qué momentos del día abarca tu jornada de trabajo? has 49063 (62.5%) missing valuesMissing
27-¿Podés cambiar los horarios de trabajo? has 48986 (62.4%) missing valuesMissing
28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan) has 48081 (61.3%) missing valuesMissing
29-no has 67247 (85.7%) missing valuesMissing
29-si has 59040 (75.2%) missing valuesMissing
30-abuelas/os has 72692 (92.6%) missing valuesMissing
30-amigas/os has 77884 (99.2%) missing valuesMissing
30-esposa/o_o_pareja has 66897 (85.2%) missing valuesMissing
30-hermanas/os has 39849 (50.8%) missing valuesMissing
30-hijas/os has 67558 (86.1%) missing valuesMissing
30-madre has 32798 (41.8%) missing valuesMissing
30-otros_(especificar) has 73082 (93.1%) missing valuesMissing
30-padre has 43374 (55.3%) missing valuesMissing
30-sobrinas/os has 75082 (95.6%) missing valuesMissing
30-vivo_sola/o has 76057 (96.9%) missing valuesMissing
31-especificar has 17160 (21.9%) missing valuesMissing
32-medio_o_polimodal has 34773 (44.3%) missing valuesMissing
32-primario_o_egb has 42505 (54.1%) missing valuesMissing
32-terciario_no_universitario has 59193 (75.4%) missing valuesMissing
32-universitarios has 63050 (80.3%) missing valuesMissing
33-no_trabajan_pero_estan_buscando_trabajo has 69840 (89.0%) missing valuesMissing
33-no_trabajan_porque_estudian has 60452 (77.0%) missing valuesMissing
33-no_trabajan_y_no_están_buscando has 69238 (88.2%) missing valuesMissing
33-son_jubilados_o_pensionados has 61590 (78.5%) missing valuesMissing
33-tienen_planes_trabajar_o_similares has 68890 (87.8%) missing valuesMissing
33-trabajan has 21572 (27.5%) missing valuesMissing
34-madre has 13799 (17.6%) missing valuesMissing
34-padre has 18619 (23.7%) missing valuesMissing
34-¿Tenés conocimientos de otros idiomas? has 15059 (19.2%) missing valuesMissing
36-especificar has 73703 (93.9%) missing valuesMissing
36-francés has 76036 (96.9%) missing valuesMissing
36-inglés has 41204 (52.5%) missing valuesMissing
36-otros has 76588 (97.6%) missing valuesMissing
36-portugués has 72116 (91.9%) missing valuesMissing
37-especificar has 35549 (45.3%) missing valuesMissing
37-otros has 62668 (79.8%) missing valuesMissing
37-tu_casa has 17121 (21.8%) missing valuesMissing
37-tu_trabajo has 51740 (65.9%) missing valuesMissing
38-especificar has 77787 (99.1%) missing valuesMissing
38-navegación_por_internet has 52424 (66.8%) missing valuesMissing
38-otros has 75196 (95.8%) missing valuesMissing
38-planilla_de_cálculo has 21771 (27.7%) missing valuesMissing
38-procesador_de_texto has 23982 (30.6%) missing valuesMissing
documento is highly skewed (γ1 = 43.9745637)Skewed
3-minutos is highly skewed (γ1 = 91.3246345)Skewed
5-me_la_recomendaron is highly skewed (γ1 = 131.5602879)Skewed
5-no_se_toma_examen_de_ingreso is highly skewed (γ1 = 153.5699415)Skewed
5-por_la_cercanía is highly skewed (γ1 = 31.3899827)Skewed
5-por_su_calidad_académica is highly skewed (γ1 = 171.1536048)Skewed
5-se_dicta_la_carrera_que_prefiero is highly skewed (γ1 = 226.2012364)Skewed
32-medio_o_polimodal is highly skewed (γ1 = 194.6905392)Skewed
32-primario_o_egb is highly skewed (γ1 = 76.97972759)Skewed
13-nombre_del_colegio is an unsupported type, check if it needs cleaning or further analysisUnsupported
14-partido/depto is an unsupported type, check if it needs cleaning or further analysisUnsupported
14-país is an unsupported type, check if it needs cleaning or further analysisUnsupported
14-provincia is an unsupported type, check if it needs cleaning or further analysisUnsupported
15-Orientación del título de Nivel Medio o Polimodal is an unsupported type, check if it needs cleaning or further analysisUnsupported
16-descripción is an unsupported type, check if it needs cleaning or further analysisUnsupported
22-¿Tu trabajo principal es? is an unsupported type, check if it needs cleaning or further analysisUnsupported
30-otros_(especificar) is an unsupported type, check if it needs cleaning or further analysisUnsupported
33-son_jubilados_o_pensionados is an unsupported type, check if it needs cleaning or further analysisUnsupported
34-padre is an unsupported type, check if it needs cleaning or further analysisUnsupported
37-especificar is an unsupported type, check if it needs cleaning or further analysisUnsupported
32-medio_o_polimodal has 1110 (1.4%) zerosZeros
32-terciario_no_universitario has 3709 (4.7%) zerosZeros
32-universitarios has 4445 (5.7%) zerosZeros
33-no_trabajan_pero_estan_buscando_trabajo has 2418 (3.1%) zerosZeros
33-no_trabajan_porque_estudian has 3450 (4.4%) zerosZeros
33-no_trabajan_y_no_están_buscando has 4416 (5.6%) zerosZeros
33-tienen_planes_trabajar_o_similares has 4428 (5.6%) zerosZeros

Reproduction

Analysis started2023-10-29 14:51:44.980126
Analysis finished2023-10-29 14:54:54.951563
Duration3 minutes and 9.97 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

carrera
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)< 0.1%
Missing7128
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean24.630246
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:55.187851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median19
Q343
95-th percentile54
Maximum58
Range57
Interquartile range (IQR)38

Descriptive statistics

Standard deviation20.526766
Coefficient of variation (CV)0.83339673
Kurtosis-1.5774113
Mean24.630246
Median Absolute Deviation (MAD)16
Skewness0.2819969
Sum1757836
Variance421.34813
MonotonicityNot monotonic
2023-10-29T11:54:55.403324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 12492
15.9%
54 11225
14.3%
5 10359
13.2%
43 9444
12.0%
27 4966
 
6.3%
52 3603
 
4.6%
17 3171
 
4.0%
25 2184
 
2.8%
4 2039
 
2.6%
2 1823
 
2.3%
Other values (17) 10063
12.8%
(Missing) 7128
9.1%
ValueCountFrequency (%)
1 1414
 
1.8%
2 1823
 
2.3%
3 12492
15.9%
4 2039
 
2.6%
5 10359
13.2%
6 199
 
0.3%
8 1430
 
1.8%
10 160
 
0.2%
15 417
 
0.5%
16 953
 
1.2%
ValueCountFrequency (%)
58 112
 
0.1%
56 1
 
< 0.1%
55 546
 
0.7%
54 11225
14.3%
52 3603
 
4.6%
51 147
 
0.2%
48 405
 
0.5%
43 9444
12.0%
41 746
 
1.0%
33 256
 
0.3%

cohorte
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.2707
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:55.626269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12016
median2019
Q32021
95-th percentile2023
Maximum2023
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.042695
Coefficient of variation (CV)0.0015075752
Kurtosis-1.1138476
Mean2018.2707
Median Absolute Deviation (MAD)2
Skewness-0.15239303
Sum1.584282 × 108
Variance9.2579927
MonotonicityIncreasing
2023-10-29T11:54:55.813944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2021 9364
11.9%
2020 8755
11.2%
2019 8055
10.3%
2017 7400
9.4%
2018 7128
9.1%
2023 6873
8.8%
2016 6536
8.3%
2022 6366
8.1%
2015 6208
7.9%
2014 5920
7.5%
ValueCountFrequency (%)
2013 5892
7.5%
2014 5920
7.5%
2015 6208
7.9%
2016 6536
8.3%
2017 7400
9.4%
2018 7128
9.1%
2019 8055
10.3%
2020 8755
11.2%
2021 9364
11.9%
2022 6366
8.1%
ValueCountFrequency (%)
2023 6873
8.8%
2022 6366
8.1%
2021 9364
11.9%
2020 8755
11.2%
2019 8055
10.3%
2018 7128
9.1%
2017 7400
9.4%
2016 6536
8.3%
2015 6208
7.9%
2014 5920
7.5%

sede
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing7128
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean2.3679609
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:56.019255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7764053
Coefficient of variation (CV)0.75018354
Kurtosis7.2952836
Mean2.3679609
Median Absolute Deviation (MAD)1
Skewness1.9640648
Sum168999
Variance3.1556157
MonotonicityNot monotonic
2023-10-29T11:54:56.218805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 28981
36.9%
2 21493
27.4%
6 9862
 
12.6%
3 8150
 
10.4%
4 2697
 
3.4%
15 72
 
0.1%
11 70
 
0.1%
21 25
 
< 0.1%
13 19
 
< 0.1%
(Missing) 7128
 
9.1%
ValueCountFrequency (%)
1 28981
36.9%
2 21493
27.4%
3 8150
 
10.4%
4 2697
 
3.4%
6 9862
 
12.6%
11 70
 
0.1%
13 19
 
< 0.1%
15 72
 
0.1%
21 25
 
< 0.1%
ValueCountFrequency (%)
21 25
 
< 0.1%
15 72
 
0.1%
13 19
 
< 0.1%
11 70
 
0.1%
6 9862
 
12.6%
4 2697
 
3.4%
3 8150
 
10.4%
2 21493
27.4%
1 28981
36.9%

documento
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct68265
Distinct (%)95.7%
Missing7128
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean40476367
Minimum40
Maximum1.754459 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:56.486245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile26040680
Q136570131
median40057297
Q343196952
95-th percentile46209768
Maximum1.754459 × 109
Range1.7544589 × 109
Interquartile range (IQR)6626821

Descriptive statistics

Standard deviation17658867
Coefficient of variation (CV)0.436276
Kurtosis3455.5721
Mean40476367
Median Absolute Deviation (MAD)3250905
Skewness43.974564
Sum2.8887578 × 1012
Variance3.118356 × 1014
MonotonicityNot monotonic
2023-10-29T11:54:56.749050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35065889 5
 
< 0.1%
37686300 5
 
< 0.1%
43399493 4
 
< 0.1%
40917516 4
 
< 0.1%
36570987 4
 
< 0.1%
36996948 4
 
< 0.1%
38679587 4
 
< 0.1%
26102769 4
 
< 0.1%
41685443 4
 
< 0.1%
29191925 4
 
< 0.1%
Other values (68255) 71327
90.9%
(Missing) 7128
 
9.1%
ValueCountFrequency (%)
40 1
< 0.1%
14681 1
< 0.1%
41390 1
< 0.1%
52237 1
< 0.1%
130921 1
< 0.1%
264208 1
< 0.1%
333945 1
< 0.1%
385771 1
< 0.1%
388322 1
< 0.1%
398020 1
< 0.1%
ValueCountFrequency (%)
1754458988 1
< 0.1%
1724795107 1
< 0.1%
1165442632 1
< 0.1%
1143834993 1
< 0.1%
1107520406 1
< 0.1%
1073518141 1
< 0.1%
1073513153 1
< 0.1%
804834422 1
< 0.1%
677064429 1
< 0.1%
605722792 1
< 0.1%

1-¿Con quien vas a vivir durante el periodo de clases?
Text

MISSING 

Distinct463
Distinct (%)0.7%
Missing12229
Missing (%)15.6%
Memory size613.4 KiB
2023-10-29T11:54:57.070125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length89
Median length19
Mean length18.160409
Min length3

Characters and Unicode

Total characters1203454
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique342 ?
Unique (%)0.5%

Sample

1st row1-mi_grupo_familiar
2nd row1-mi_grupo_familiar
3rd row1-mi_grupo_familiar
4th row1-mi_grupo_familiar
5th row1-mi_grupo_familiar
ValueCountFrequency (%)
1-mi_grupo_familiar 58234
85.8%
1-sola/o 4117
 
6.1%
1-algún_pariente 1367
 
2.0%
1-compañeras/os_o_amigas/os 833
 
1.2%
pareja 772
 
1.1%
mi 610
 
0.9%
con 325
 
0.5%
novio 170
 
0.3%
1-familia_amiga_de_mi_familia 129
 
0.2%
novia 73
 
0.1%
Other values (271) 1277
 
1.9%
2023-10-29T11:54:57.646487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 179017
14.9%
a 129741
10.8%
_ 120017
10.0%
r 119713
9.9%
m 119345
9.9%
o 71196
 
5.9%
1 64688
 
5.4%
- 64680
 
5.4%
l 64126
 
5.3%
p 61043
 
5.1%
Other values (59) 209888
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 945051
78.5%
Connector Punctuation 120017
 
10.0%
Decimal Number 64705
 
5.4%
Dash Punctuation 64680
 
5.4%
Other Punctuation 5841
 
0.5%
Space Separator 1712
 
0.1%
Uppercase Letter 1434
 
0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 179017
18.9%
a 129741
13.7%
r 119713
12.7%
m 119345
12.6%
o 71196
 
7.5%
l 64126
 
6.8%
p 61043
 
6.5%
g 60644
 
6.4%
f 58522
 
6.2%
u 58456
 
6.2%
Other values (20) 23248
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
P 317
22.1%
M 249
17.4%
C 240
16.7%
N 120
 
8.4%
A 95
 
6.6%
E 87
 
6.1%
O 47
 
3.3%
I 46
 
3.2%
H 38
 
2.6%
R 37
 
2.6%
Other values (13) 158
11.0%
Decimal Number
ValueCountFrequency (%)
1 64688
> 99.9%
2 5
 
< 0.1%
4 4
 
< 0.1%
3 2
 
< 0.1%
9 2
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 5785
99.0%
. 40
 
0.7%
, 16
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_ 120017
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64680
100.0%
Space Separator
ValueCountFrequency (%)
1712
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 946485
78.6%
Common 256969
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 179017
18.9%
a 129741
13.7%
r 119713
12.6%
m 119345
12.6%
o 71196
 
7.5%
l 64126
 
6.8%
p 61043
 
6.4%
g 60644
 
6.4%
f 58522
 
6.2%
u 58456
 
6.2%
Other values (43) 24682
 
2.6%
Common
ValueCountFrequency (%)
_ 120017
46.7%
1 64688
25.2%
- 64680
25.2%
/ 5785
 
2.3%
1712
 
0.7%
. 40
 
< 0.1%
, 16
 
< 0.1%
) 7
 
< 0.1%
( 7
 
< 0.1%
2 5
 
< 0.1%
Other values (6) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1201138
99.8%
None 2316
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 179017
14.9%
a 129741
10.8%
_ 120017
10.0%
r 119713
10.0%
m 119345
9.9%
o 71196
 
5.9%
1 64688
 
5.4%
- 64680
 
5.4%
l 64126
 
5.3%
p 61043
 
5.1%
Other values (52) 207572
17.3%
None
ValueCountFrequency (%)
ú 1371
59.2%
ñ 854
36.9%
á 31
 
1.3%
í 30
 
1.3%
ó 19
 
0.8%
é 10
 
0.4%
Ó 1
 
< 0.1%

2-¿Por qué medio te trasladarás hasta la sede donde va a cursar durante el periodo de clases?
Text

MISSING 

Distinct104
Distinct (%)0.2%
Missing12270
Missing (%)15.6%
Memory size613.4 KiB
2023-10-29T11:54:57.902685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length98
Median length87
Mean length21.886119
Min length4

Characters and Unicode

Total characters1449452
Distinct characters60
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.1%

Sample

1st row2-en_colectivo_de_corta_distancia
2nd row2-en_colectivo_de_media_distancia
3rd row2-en_colectivo_de_corta_distancia
4th row2-en_auto
5th row2-en_colectivo_de_corta_distancia
ValueCountFrequency (%)
2-en_colectivo_de_corta_distancia 16218
24.4%
2-en_colectivo_de_media_distancia 14358
21.6%
2-en_tren 12625
19.0%
2-a_pie 10276
15.4%
2-en_bicicleta_o_moto 6574
9.9%
2-en_auto 3545
 
5.3%
2-en_colectivo_de_larga_distancia 1936
 
2.9%
2-en_colectivo_o_combi_contratada 558
 
0.8%
colectivo 58
 
0.1%
en 53
 
0.1%
Other values (119) 381
 
0.6%
2023-10-29T11:54:58.506333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 178448
12.3%
e 165468
11.4%
i 136618
9.4%
c 129320
8.9%
a 121695
8.4%
t 112455
7.8%
o 107606
7.4%
n 101667
7.0%
d 80007
 
5.5%
2 66095
 
4.6%
Other values (50) 250073
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1138288
78.5%
Connector Punctuation 178448
 
12.3%
Decimal Number 66104
 
4.6%
Dash Punctuation 66091
 
4.6%
Space Separator 362
 
< 0.1%
Uppercase Letter 138
 
< 0.1%
Other Punctuation 15
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 165468
14.5%
i 136618
12.0%
c 129320
11.4%
a 121695
10.7%
t 112455
9.9%
o 107606
9.5%
n 101667
8.9%
d 80007
7.0%
l 41685
 
3.7%
v 33144
 
2.9%
Other values (18) 108623
9.5%
Uppercase Letter
ValueCountFrequency (%)
E 37
26.8%
C 20
14.5%
M 19
13.8%
T 11
 
8.0%
L 7
 
5.1%
A 7
 
5.1%
S 6
 
4.3%
O 6
 
4.3%
N 5
 
3.6%
V 5
 
3.6%
Other values (7) 15
10.9%
Decimal Number
ValueCountFrequency (%)
2 66095
> 99.9%
7 3
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
1 1
 
< 0.1%
0 1
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
, 12
80.0%
. 2
 
13.3%
/ 1
 
6.7%
Connector Punctuation
ValueCountFrequency (%)
_ 178448
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66091
100.0%
Space Separator
ValueCountFrequency (%)
362
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1138426
78.5%
Common 311026
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 165468
14.5%
i 136618
12.0%
c 129320
11.4%
a 121695
10.7%
t 112455
9.9%
o 107606
9.5%
n 101667
8.9%
d 80007
7.0%
l 41685
 
3.7%
v 33144
 
2.9%
Other values (35) 108761
9.6%
Common
ValueCountFrequency (%)
_ 178448
57.4%
2 66095
 
21.3%
- 66091
 
21.2%
362
 
0.1%
, 12
 
< 0.1%
7 3
 
< 0.1%
) 3
 
< 0.1%
( 3
 
< 0.1%
. 2
 
< 0.1%
5 2
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1449441
> 99.9%
None 11
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 178448
12.3%
e 165468
11.4%
i 136618
9.4%
c 129320
8.9%
a 121695
8.4%
t 112455
7.8%
o 107606
7.4%
n 101667
7.0%
d 80007
 
5.5%
2 66095
 
4.6%
Other values (45) 250062
17.3%
None
ValueCountFrequency (%)
ú 4
36.4%
í 2
18.2%
é 2
18.2%
á 2
18.2%
ñ 1
 
9.1%

3-minutos
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct161
Distinct (%)0.3%
Missing16128
Missing (%)20.5%
Infinite0
Infinite (%)0.0%
Mean49.456653
Minimum0
Maximum12132
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:58.722663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q130
median40
Q360
95-th percentile120
Maximum12132
Range12132
Interquartile range (IQR)30

Descriptive statistics

Standard deviation76.5097
Coefficient of variation (CV)1.5470052
Kurtosis12045.254
Mean49.456653
Median Absolute Deviation (MAD)20
Skewness91.324635
Sum3084562
Variance5853.7341
MonotonicityNot monotonic
2023-10-29T11:54:58.828470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 11142
14.2%
60 10666
13.6%
40 7924
10.1%
20 5691
 
7.2%
45 4050
 
5.2%
90 4037
 
5.1%
15 3516
 
4.5%
120 3189
 
4.1%
50 2873
 
3.7%
10 1999
 
2.5%
Other values (151) 7282
9.3%
(Missing) 16128
20.5%
ValueCountFrequency (%)
0 16
 
< 0.1%
1 170
 
0.2%
2 111
 
0.1%
3 33
 
< 0.1%
4 11
 
< 0.1%
5 461
0.6%
6 10
 
< 0.1%
7 29
 
< 0.1%
8 37
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
12132 1
 
< 0.1%
7200 1
 
< 0.1%
5460 1
 
< 0.1%
4000 3
< 0.1%
3600 1
 
< 0.1%
2880 1
 
< 0.1%
1440 1
 
< 0.1%
1120 1
 
< 0.1%
1080 2
< 0.1%
720 1
 
< 0.1%

4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar?
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing12672
Missing (%)16.1%
Memory size613.4 KiB
4-mucho
38427 
4-poco
21449 
4-nada
5949 

Length

Max length7
Median length7
Mean length6.5837752
Min length6

Characters and Unicode

Total characters433377
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4-nada
2nd row4-mucho
3rd row4-mucho
4th row4-mucho
5th row4-mucho

Common Values

ValueCountFrequency (%)
4-mucho 38427
49.0%
4-poco 21449
27.3%
4-nada 5949
 
7.6%
(Missing) 12672
 
16.1%

Length

2023-10-29T11:54:58.915272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:54:59.055841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4-mucho 38427
58.4%
4-poco 21449
32.6%
4-nada 5949
 
9.0%

Most occurring characters

ValueCountFrequency (%)
o 81325
18.8%
4 65825
15.2%
- 65825
15.2%
c 59876
13.8%
m 38427
8.9%
u 38427
8.9%
h 38427
8.9%
p 21449
 
4.9%
a 11898
 
2.7%
n 5949
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 301727
69.6%
Decimal Number 65825
 
15.2%
Dash Punctuation 65825
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 81325
27.0%
c 59876
19.8%
m 38427
12.7%
u 38427
12.7%
h 38427
12.7%
p 21449
 
7.1%
a 11898
 
3.9%
n 5949
 
2.0%
d 5949
 
2.0%
Decimal Number
ValueCountFrequency (%)
4 65825
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301727
69.6%
Common 131650
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 81325
27.0%
c 59876
19.8%
m 38427
12.7%
u 38427
12.7%
h 38427
12.7%
p 21449
 
7.1%
a 11898
 
3.9%
n 5949
 
2.0%
d 5949
 
2.0%
Common
ValueCountFrequency (%)
4 65825
50.0%
- 65825
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 433377
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 81325
18.8%
4 65825
15.2%
- 65825
15.2%
c 59876
13.8%
m 38427
8.9%
u 38427
8.9%
h 38427
8.9%
p 21449
 
4.9%
a 11898
 
2.7%
n 5949
 
1.4%

5-es_gratuita
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)< 0.1%
Missing29096
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean2.3481508
Minimum0
Maximum67
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:59.136542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum67
Range67
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6151306
Coefficient of variation (CV)0.68783085
Kurtosis121.51065
Mean2.3481508
Median Absolute Deviation (MAD)1
Skewness4.4894433
Sum116001
Variance2.6086468
MonotonicityNot monotonic
2023-10-29T11:54:59.216753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 19313
24.6%
2 12659
16.1%
3 7727
 
9.8%
4 4181
 
5.3%
5 2702
 
3.4%
6 2186
 
2.8%
7 511
 
0.7%
8 63
 
0.1%
10 24
 
< 0.1%
9 14
 
< 0.1%
Other values (9) 21
 
< 0.1%
(Missing) 29096
37.1%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 19313
24.6%
2 12659
16.1%
3 7727
9.8%
4 4181
 
5.3%
5 2702
 
3.4%
6 2186
 
2.8%
7 511
 
0.7%
8 63
 
0.1%
9 14
 
< 0.1%
ValueCountFrequency (%)
67 1
 
< 0.1%
63 1
 
< 0.1%
54 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
23 1
 
< 0.1%
11 8
 
< 0.1%
10 24
< 0.1%
9 14
< 0.1%

5-me_la_recomendaron
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct17
Distinct (%)< 0.1%
Missing43480
Missing (%)55.4%
Infinite0
Infinite (%)0.0%
Mean10.649113
Minimum0
Maximum123456
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:59.291509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum123456
Range123456
Interquartile range (IQR)2

Descriptive statistics

Standard deviation934.86449
Coefficient of variation (CV)87.788012
Kurtosis17365.264
Mean10.649113
Median Absolute Deviation (MAD)1
Skewness131.56029
Sum372900
Variance873971.62
MonotonicityNot monotonic
2023-10-29T11:54:59.383795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 7832
 
10.0%
3 6775
 
8.6%
1 6167
 
7.9%
4 5615
 
7.2%
5 3783
 
4.8%
6 2827
 
3.6%
7 1851
 
2.4%
8 122
 
0.2%
9 15
 
< 0.1%
10 13
 
< 0.1%
Other values (7) 17
 
< 0.1%
(Missing) 43480
55.4%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 6167
7.9%
2 7832
10.0%
3 6775
8.6%
4 5615
7.2%
5 3783
4.8%
6 2827
 
3.6%
7 1851
 
2.4%
8 122
 
0.2%
9 15
 
< 0.1%
ValueCountFrequency (%)
123456 2
 
< 0.1%
11122 1
 
< 0.1%
66 1
 
< 0.1%
56 1
 
< 0.1%
31 1
 
< 0.1%
11 2
 
< 0.1%
10 13
 
< 0.1%
9 15
 
< 0.1%
8 122
 
0.2%
7 1851
2.4%

5-no_se_toma_examen_de_ingreso
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct13
Distinct (%)0.1%
Missing54773
Missing (%)69.8%
Infinite0
Infinite (%)0.0%
Mean4.8614062
Minimum0
Maximum7777
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:54:59.981736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q37
95-th percentile7
Maximum7777
Range7777
Interquartile range (IQR)5

Descriptive statistics

Standard deviation50.511944
Coefficient of variation (CV)10.390398
Kurtosis23630.391
Mean4.8614062
Median Absolute Deviation (MAD)2
Skewness153.56994
Sum115332
Variance2551.4565
MonotonicityNot monotonic
2023-10-29T11:55:00.160887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 6589
 
8.4%
2 3866
 
4.9%
6 2740
 
3.5%
3 2696
 
3.4%
4 2517
 
3.2%
1 2470
 
3.1%
5 2028
 
2.6%
8 768
 
1.0%
9 22
 
< 0.1%
10 14
 
< 0.1%
Other values (3) 14
 
< 0.1%
(Missing) 54773
69.8%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 2470
 
3.1%
2 3866
4.9%
3 2696
3.4%
4 2517
 
3.2%
5 2028
 
2.6%
6 2740
3.5%
7 6589
8.4%
8 768
 
1.0%
9 22
 
< 0.1%
ValueCountFrequency (%)
7777 1
 
< 0.1%
12 1
 
< 0.1%
10 14
 
< 0.1%
9 22
 
< 0.1%
8 768
 
1.0%
7 6589
8.4%
6 2740
3.5%
5 2028
 
2.6%
4 2517
 
3.2%
3 2696
3.4%

5-otras_razones
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)0.1%
Missing66513
Missing (%)84.7%
Infinite0
Infinite (%)0.0%
Mean6.4110481
Minimum0
Maximum88
Zeros54
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:00.252903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median8
Q38
95-th percentile8
Maximum88
Range88
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7304138
Coefficient of variation (CV)0.42589196
Kurtosis106.19141
Mean6.4110481
Median Absolute Deviation (MAD)0
Skewness2.9159618
Sum76830
Variance7.4551596
MonotonicityNot monotonic
2023-10-29T11:55:00.332834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8 7574
 
9.6%
2 1347
 
1.7%
7 883
 
1.1%
1 645
 
0.8%
3 573
 
0.7%
4 329
 
0.4%
5 249
 
0.3%
6 242
 
0.3%
9 77
 
0.1%
0 54
 
0.1%
Other values (5) 11
 
< 0.1%
(Missing) 66513
84.7%
ValueCountFrequency (%)
0 54
 
0.1%
1 645
 
0.8%
2 1347
 
1.7%
3 573
 
0.7%
4 329
 
0.4%
5 249
 
0.3%
6 242
 
0.3%
7 883
 
1.1%
8 7574
9.6%
9 77
 
0.1%
ValueCountFrequency (%)
88 1
 
< 0.1%
78 1
 
< 0.1%
36 1
 
< 0.1%
21 1
 
< 0.1%
10 7
 
< 0.1%
9 77
 
0.1%
8 7574
9.6%
7 883
 
1.1%
6 242
 
0.3%
5 249
 
0.3%

5-por_el_prestigio_de_la_carrera
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)0.1%
Missing52349
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean3.7474377
Minimum0
Maximum67
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:00.409386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0215695
Coefficient of variation (CV)0.53945381
Kurtosis35.646925
Mean3.7474377
Median Absolute Deviation (MAD)2
Skewness1.3821485
Sum97988
Variance4.0867433
MonotonicityNot monotonic
2023-10-29T11:55:00.506611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 4606
 
5.9%
1 4187
 
5.3%
3 4002
 
5.1%
4 3531
 
4.5%
6 3511
 
4.5%
5 3503
 
4.5%
7 2667
 
3.4%
8 118
 
0.2%
9 10
 
< 0.1%
10 6
 
< 0.1%
Other values (5) 7
 
< 0.1%
(Missing) 52349
66.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 4187
5.3%
2 4606
5.9%
3 4002
5.1%
4 3531
4.5%
5 3503
4.5%
6 3511
4.5%
7 2667
3.4%
8 118
 
0.2%
9 10
 
< 0.1%
ValueCountFrequency (%)
67 1
 
< 0.1%
22 1
 
< 0.1%
15 1
 
< 0.1%
11 1
 
< 0.1%
10 6
 
< 0.1%
9 10
 
< 0.1%
8 118
 
0.2%
7 2667
3.4%
6 3511
4.5%
5 3503
4.5%

5-por_la_cercanía
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct20
Distinct (%)< 0.1%
Missing34384
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean2.7936889
Minimum0
Maximum211
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:00.604573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum211
Range211
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9743054
Coefficient of variation (CV)0.70670194
Kurtosis3016.5817
Mean2.7936889
Median Absolute Deviation (MAD)1
Skewness31.389983
Sum123238
Variance3.8978818
MonotonicityNot monotonic
2023-10-29T11:55:00.675262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 14070
17.9%
3 9940
 
12.7%
1 9169
 
11.7%
4 4663
 
5.9%
5 2706
 
3.4%
6 1730
 
2.2%
7 1548
 
2.0%
8 247
 
0.3%
9 12
 
< 0.1%
10 10
 
< 0.1%
Other values (10) 18
 
< 0.1%
(Missing) 34384
43.8%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 9169
11.7%
2 14070
17.9%
3 9940
12.7%
4 4663
 
5.9%
5 2706
 
3.4%
6 1730
 
2.2%
7 1548
 
2.0%
8 247
 
0.3%
9 12
 
< 0.1%
ValueCountFrequency (%)
211 1
 
< 0.1%
111 1
 
< 0.1%
45 1
 
< 0.1%
39 1
 
< 0.1%
33 1
 
< 0.1%
23 1
 
< 0.1%
21 2
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 10
< 0.1%

5-por_su_calidad_académica
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct16
Distinct (%)< 0.1%
Missing41510
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean2.8362398
Minimum0
Maximum1111
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:00.758607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum1111
Range1111
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.9911548
Coefficient of variation (CV)2.1123584
Kurtosis31649.778
Mean2.8362398
Median Absolute Deviation (MAD)1
Skewness171.1536
Sum104904
Variance35.893936
MonotonicityNot monotonic
2023-10-29T11:55:00.842315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 9478
 
12.1%
2 9222
 
11.7%
3 7024
 
8.9%
4 5021
 
6.4%
5 3547
 
4.5%
6 2077
 
2.6%
7 523
 
0.7%
8 53
 
0.1%
10 20
 
< 0.1%
9 12
 
< 0.1%
Other values (6) 10
 
< 0.1%
(Missing) 41510
52.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 9478
12.1%
2 9222
11.7%
3 7024
8.9%
4 5021
6.4%
5 3547
 
4.5%
6 2077
 
2.6%
7 523
 
0.7%
8 53
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
1111 1
 
< 0.1%
67 1
 
< 0.1%
23 1
 
< 0.1%
13 1
 
< 0.1%
11 2
 
< 0.1%
10 20
 
< 0.1%
9 12
 
< 0.1%
8 53
 
0.1%
7 523
 
0.7%
6 2077
2.6%

5-se_dicta_la_carrera_que_prefiero
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct18
Distinct (%)< 0.1%
Missing27330
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean353.6829
Minimum0
Maximum17999006
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:00.932273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum17999006
Range17999006
Interquartile range (IQR)1

Descriptive statistics

Standard deviation79570.759
Coefficient of variation (CV)224.97768
Kurtosis51167
Mean353.6829
Median Absolute Deviation (MAD)0
Skewness226.20124
Sum18096893
Variance6.3315056 × 109
MonotonicityNot monotonic
2023-10-29T11:55:01.013240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 27615
35.2%
2 11954
15.2%
3 6048
 
7.7%
4 2758
 
3.5%
5 1504
 
1.9%
6 840
 
1.1%
7 343
 
0.4%
8 59
 
0.1%
10 24
 
< 0.1%
9 8
 
< 0.1%
Other values (8) 14
 
< 0.1%
(Missing) 27330
34.8%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 27615
35.2%
2 11954
15.2%
3 6048
 
7.7%
4 2758
 
3.5%
5 1504
 
1.9%
6 840
 
1.1%
7 343
 
0.4%
8 59
 
0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
17999006 1
 
< 0.1%
1111 1
 
< 0.1%
121 1
 
< 0.1%
111 1
 
< 0.1%
44 1
 
< 0.1%
12 1
 
< 0.1%
11 4
 
< 0.1%
10 24
< 0.1%
9 8
 
< 0.1%
8 59
0.1%

6-medio_o_forma_por_el_cual_conoció_la_universidad
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing77663
Missing (%)98.9%
Memory size613.4 KiB
6-medio_o_forma_por_el_cual_conoció_la_universidad
834 

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters41700
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6-medio_o_forma_por_el_cual_conoció_la_universidad
2nd row6-medio_o_forma_por_el_cual_conoció_la_universidad
3rd row6-medio_o_forma_por_el_cual_conoció_la_universidad
4th row6-medio_o_forma_por_el_cual_conoció_la_universidad
5th row6-medio_o_forma_por_el_cual_conoció_la_universidad

Common Values

ValueCountFrequency (%)
6-medio_o_forma_por_el_cual_conoció_la_universidad 834
 
1.1%
(Missing) 77663
98.9%

Length

2023-10-29T11:55:01.112540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:01.213748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
6-medio_o_forma_por_el_cual_conoció_la_universidad 834
100.0%

Most occurring characters

ValueCountFrequency (%)
_ 6672
16.0%
o 5004
12.0%
i 3336
 
8.0%
a 3336
 
8.0%
r 2502
 
6.0%
e 2502
 
6.0%
d 2502
 
6.0%
l 2502
 
6.0%
c 2502
 
6.0%
u 1668
 
4.0%
Other values (9) 9174
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33360
80.0%
Connector Punctuation 6672
 
16.0%
Decimal Number 834
 
2.0%
Dash Punctuation 834
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5004
15.0%
i 3336
10.0%
a 3336
10.0%
r 2502
7.5%
e 2502
7.5%
d 2502
7.5%
l 2502
7.5%
c 2502
7.5%
u 1668
 
5.0%
m 1668
 
5.0%
Other values (6) 5838
17.5%
Connector Punctuation
ValueCountFrequency (%)
_ 6672
100.0%
Decimal Number
ValueCountFrequency (%)
6 834
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33360
80.0%
Common 8340
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5004
15.0%
i 3336
10.0%
a 3336
10.0%
r 2502
7.5%
e 2502
7.5%
d 2502
7.5%
l 2502
7.5%
c 2502
7.5%
u 1668
 
5.0%
m 1668
 
5.0%
Other values (6) 5838
17.5%
Common
ValueCountFrequency (%)
_ 6672
80.0%
6 834
 
10.0%
- 834
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40866
98.0%
None 834
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6672
16.3%
o 5004
12.2%
i 3336
 
8.2%
a 3336
 
8.2%
r 2502
 
6.1%
e 2502
 
6.1%
d 2502
 
6.1%
l 2502
 
6.1%
c 2502
 
6.1%
u 1668
 
4.1%
Other values (8) 8340
20.4%
None
ValueCountFrequency (%)
ó 834
100.0%

7-¿Qué nivel estás cursando?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing13115
Missing (%)16.7%
Memory size613.4 KiB
7-no
40242 
7-si
25140 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters261528
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7-no
2nd row7-no
3rd row7-si
4th row7-no
5th row7-no

Common Values

ValueCountFrequency (%)
7-no 40242
51.3%
7-si 25140
32.0%
(Missing) 13115
 
16.7%

Length

2023-10-29T11:55:01.299405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:01.391514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7-no 40242
61.5%
7-si 25140
38.5%

Most occurring characters

ValueCountFrequency (%)
7 65382
25.0%
- 65382
25.0%
n 40242
15.4%
o 40242
15.4%
s 25140
 
9.6%
i 25140
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130764
50.0%
Decimal Number 65382
25.0%
Dash Punctuation 65382
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 40242
30.8%
o 40242
30.8%
s 25140
19.2%
i 25140
19.2%
Decimal Number
ValueCountFrequency (%)
7 65382
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130764
50.0%
Latin 130764
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 40242
30.8%
o 40242
30.8%
s 25140
19.2%
i 25140
19.2%
Common
ValueCountFrequency (%)
7 65382
50.0%
- 65382
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 65382
25.0%
- 65382
25.0%
n 40242
15.4%
o 40242
15.4%
s 25140
 
9.6%
i 25140
 
9.6%

8-¿Cuál fue el nivel mas alto que terminaste?
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing54729
Missing (%)69.7%
Memory size613.4 KiB
8-medio/polimodal
18502 
8-terciario
2673 
8-universitario
2593 

Length

Max length17
Median length17
Mean length16.107035
Min length11

Characters and Unicode

Total characters382832
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8-medio/polimodal
2nd row8-medio/polimodal
3rd row8-medio/polimodal
4th row8-medio/polimodal
5th row8-medio/polimodal

Common Values

ValueCountFrequency (%)
8-medio/polimodal 18502
 
23.6%
8-terciario 2673
 
3.4%
8-universitario 2593
 
3.3%
(Missing) 54729
69.7%

Length

2023-10-29T11:55:01.472039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:01.600284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8-medio/polimodal 18502
77.8%
8-terciario 2673
 
11.2%
8-universitario 2593
 
10.9%

Most occurring characters

ValueCountFrequency (%)
o 60772
15.9%
i 50129
13.1%
l 37004
9.7%
m 37004
9.7%
d 37004
9.7%
- 23768
 
6.2%
a 23768
 
6.2%
8 23768
 
6.2%
e 23768
 
6.2%
p 18502
 
4.8%
Other values (8) 47345
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316794
82.8%
Dash Punctuation 23768
 
6.2%
Decimal Number 23768
 
6.2%
Other Punctuation 18502
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 60772
19.2%
i 50129
15.8%
l 37004
11.7%
m 37004
11.7%
d 37004
11.7%
a 23768
 
7.5%
e 23768
 
7.5%
p 18502
 
5.8%
r 10532
 
3.3%
t 5266
 
1.7%
Other values (5) 13045
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 23768
100.0%
Decimal Number
ValueCountFrequency (%)
8 23768
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 18502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316794
82.8%
Common 66038
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 60772
19.2%
i 50129
15.8%
l 37004
11.7%
m 37004
11.7%
d 37004
11.7%
a 23768
 
7.5%
e 23768
 
7.5%
p 18502
 
5.8%
r 10532
 
3.3%
t 5266
 
1.7%
Other values (5) 13045
 
4.1%
Common
ValueCountFrequency (%)
- 23768
36.0%
8 23768
36.0%
/ 18502
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 60772
15.9%
i 50129
13.1%
l 37004
9.7%
m 37004
9.7%
d 37004
9.7%
- 23768
 
6.2%
a 23768
 
6.2%
8 23768
 
6.2%
e 23768
 
6.2%
p 18502
 
4.8%
Other values (8) 47345
12.4%

9-¿Terminaste o estás cursando tus estudios en un Colegio?
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing14988
Missing (%)19.1%
Memory size613.4 KiB
9-medio/polimodal
47659 
9-terciario
7417 
9-primario
7023 
9-universitario
 
1410

Length

Max length17
Median length17
Mean length15.480798
Min length10

Characters and Unicode

Total characters983170
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9-medio/polimodal
2nd row9-medio/polimodal
3rd row9-primario
4th row9-terciario
5th row9-medio/polimodal

Common Values

ValueCountFrequency (%)
9-medio/polimodal 47659
60.7%
9-terciario 7417
 
9.4%
9-primario 7023
 
8.9%
9-universitario 1410
 
1.8%
(Missing) 14988
 
19.1%

Length

2023-10-29T11:55:01.694829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:01.799309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9-medio/polimodal 47659
75.0%
9-terciario 7417
 
11.7%
9-primario 7023
 
11.1%
9-universitario 1410
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o 158827
16.2%
i 128428
13.1%
m 102341
10.4%
l 95318
9.7%
d 95318
9.7%
- 63509
 
6.5%
a 63509
 
6.5%
9 63509
 
6.5%
e 56486
 
5.7%
p 54682
 
5.6%
Other values (8) 101243
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 808493
82.2%
Dash Punctuation 63509
 
6.5%
Decimal Number 63509
 
6.5%
Other Punctuation 47659
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 158827
19.6%
i 128428
15.9%
m 102341
12.7%
l 95318
11.8%
d 95318
11.8%
a 63509
 
7.9%
e 56486
 
7.0%
p 54682
 
6.8%
r 31700
 
3.9%
t 8827
 
1.1%
Other values (5) 13057
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 63509
100.0%
Decimal Number
ValueCountFrequency (%)
9 63509
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 47659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 808493
82.2%
Common 174677
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 158827
19.6%
i 128428
15.9%
m 102341
12.7%
l 95318
11.8%
d 95318
11.8%
a 63509
 
7.9%
e 56486
 
7.0%
p 54682
 
6.8%
r 31700
 
3.9%
t 8827
 
1.1%
Other values (5) 13057
 
1.6%
Common
ValueCountFrequency (%)
- 63509
36.4%
9 63509
36.4%
/ 47659
27.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 158827
16.2%
i 128428
13.1%
m 102341
10.4%
l 95318
9.7%
d 95318
9.7%
- 63509
 
6.5%
a 63509
 
6.5%
9 63509
 
6.5%
e 56486
 
5.7%
p 54682
 
5.6%
Other values (8) 101243
10.3%

10-¿El Colegio es… (Privado/Público)
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing55365
Missing (%)70.5%
Memory size613.4 KiB
10-público
12323 
10-privado
10809 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters231320
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10-público
2nd row10-público
3rd row10-público
4th row10-público
5th row10-público

Common Values

ValueCountFrequency (%)
10-público 12323
 
15.7%
10-privado 10809
 
13.8%
(Missing) 55365
70.5%

Length

2023-10-29T11:55:01.893676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:01.986575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10-público 12323
53.3%
10-privado 10809
46.7%

Most occurring characters

ValueCountFrequency (%)
1 23132
10.0%
0 23132
10.0%
- 23132
10.0%
p 23132
10.0%
i 23132
10.0%
o 23132
10.0%
ú 12323
 
5.3%
b 12323
 
5.3%
l 12323
 
5.3%
c 12323
 
5.3%
Other values (4) 43236
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 161924
70.0%
Decimal Number 46264
 
20.0%
Dash Punctuation 23132
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 23132
14.3%
i 23132
14.3%
o 23132
14.3%
ú 12323
7.6%
b 12323
7.6%
l 12323
7.6%
c 12323
7.6%
r 10809
6.7%
v 10809
6.7%
a 10809
6.7%
Decimal Number
ValueCountFrequency (%)
1 23132
50.0%
0 23132
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 23132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161924
70.0%
Common 69396
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 23132
14.3%
i 23132
14.3%
o 23132
14.3%
ú 12323
7.6%
b 12323
7.6%
l 12323
7.6%
c 12323
7.6%
r 10809
6.7%
v 10809
6.7%
a 10809
6.7%
Common
ValueCountFrequency (%)
1 23132
33.3%
0 23132
33.3%
- 23132
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218997
94.7%
None 12323
 
5.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 23132
10.6%
0 23132
10.6%
- 23132
10.6%
p 23132
10.6%
i 23132
10.6%
o 23132
10.6%
b 12323
 
5.6%
l 12323
 
5.6%
c 12323
 
5.6%
r 10809
 
4.9%
Other values (3) 32427
14.8%
None
ValueCountFrequency (%)
ú 12323
100.0%

11-Número del Colegio
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing24198
Missing (%)30.8%
Memory size613.4 KiB
11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)
45270 
11-escuela_de_educación_técnica_(ex-enet)
4844 
11-cens
 
3291
11-escuela_de_educación_agraria_(eea)
 
783
11-otros_(especificar)
 
111

Length

Max length77
Median length77
Mean length68.85659
Min length7

Characters and Unicode

Total characters3738844
Distinct characters25
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)
2nd row11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)
3rd row11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)
4th row11-cens
5th row11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)

Common Values

ValueCountFrequency (%)
11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional) 45270
57.7%
11-escuela_de_educación_técnica_(ex-enet) 4844
 
6.2%
11-cens 3291
 
4.2%
11-escuela_de_educación_agraria_(eea) 783
 
1.0%
11-otros_(especificar) 111
 
0.1%
(Missing) 24198
30.8%

Length

2023-10-29T11:55:02.066604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:02.196335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional 45270
83.4%
11-escuela_de_educación_técnica_(ex-enet 4844
 
8.9%
11-cens 3291
 
6.1%
11-escuela_de_educación_agraria_(eea 783
 
1.4%
11-otros_(especificar 111
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 404279
10.8%
_ 384779
10.3%
a 381501
10.2%
c 301702
 
8.1%
l 277247
 
7.4%
o 271842
 
7.3%
i 237826
 
6.4%
n 199686
 
5.3%
d 192334
 
5.1%
m 181080
 
4.8%
Other values (15) 906568
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2903228
77.7%
Connector Punctuation 384779
 
10.3%
Dash Punctuation 149683
 
4.0%
Decimal Number 108598
 
2.9%
Other Punctuation 90540
 
2.4%
Open Punctuation 51008
 
1.4%
Close Punctuation 51008
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 404279
13.9%
a 381501
13.1%
c 301702
10.4%
l 277247
9.5%
o 271842
9.4%
i 237826
8.2%
n 199686
6.9%
d 192334
6.6%
m 181080
6.2%
u 101794
 
3.5%
Other values (9) 353937
12.2%
Connector Punctuation
ValueCountFrequency (%)
_ 384779
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149683
100.0%
Decimal Number
ValueCountFrequency (%)
1 108598
100.0%
Other Punctuation
ValueCountFrequency (%)
, 90540
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51008
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51008
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2903228
77.7%
Common 835616
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 404279
13.9%
a 381501
13.1%
c 301702
10.4%
l 277247
9.5%
o 271842
9.4%
i 237826
8.2%
n 199686
6.9%
d 192334
6.6%
m 181080
6.2%
u 101794
 
3.5%
Other values (9) 353937
12.2%
Common
ValueCountFrequency (%)
_ 384779
46.0%
- 149683
 
17.9%
1 108598
 
13.0%
, 90540
 
10.8%
( 51008
 
6.1%
) 51008
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3683103
98.5%
None 55741
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 404279
11.0%
_ 384779
10.4%
a 381501
10.4%
c 301702
 
8.2%
l 277247
 
7.5%
o 271842
 
7.4%
i 237826
 
6.5%
n 199686
 
5.4%
d 192334
 
5.2%
m 181080
 
4.9%
Other values (13) 850827
23.1%
None
ValueCountFrequency (%)
ó 50897
91.3%
é 4844
 
8.7%

12-número_del_colegio
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing73433
Missing (%)93.5%
Memory size613.4 KiB
12-número_del_colegio
5064 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters106344
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12-número_del_colegio
2nd row12-número_del_colegio
3rd row12-número_del_colegio
4th row12-número_del_colegio
5th row12-número_del_colegio

Common Values

ValueCountFrequency (%)
12-número_del_colegio 5064
 
6.5%
(Missing) 73433
93.5%

Length

2023-10-29T11:55:02.309493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:02.400442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
12-número_del_colegio 5064
100.0%

Most occurring characters

ValueCountFrequency (%)
e 15192
14.3%
o 15192
14.3%
_ 10128
 
9.5%
l 10128
 
9.5%
1 5064
 
4.8%
2 5064
 
4.8%
- 5064
 
4.8%
n 5064
 
4.8%
ú 5064
 
4.8%
m 5064
 
4.8%
Other values (5) 25320
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81024
76.2%
Connector Punctuation 10128
 
9.5%
Decimal Number 10128
 
9.5%
Dash Punctuation 5064
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15192
18.8%
o 15192
18.8%
l 10128
12.5%
n 5064
 
6.2%
ú 5064
 
6.2%
m 5064
 
6.2%
r 5064
 
6.2%
d 5064
 
6.2%
c 5064
 
6.2%
g 5064
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 5064
50.0%
2 5064
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81024
76.2%
Common 25320
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15192
18.8%
o 15192
18.8%
l 10128
12.5%
n 5064
 
6.2%
ú 5064
 
6.2%
m 5064
 
6.2%
r 5064
 
6.2%
d 5064
 
6.2%
c 5064
 
6.2%
g 5064
 
6.2%
Common
ValueCountFrequency (%)
_ 10128
40.0%
1 5064
20.0%
2 5064
20.0%
- 5064
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101280
95.2%
None 5064
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15192
15.0%
o 15192
15.0%
_ 10128
10.0%
l 10128
10.0%
1 5064
 
5.0%
2 5064
 
5.0%
- 5064
 
5.0%
n 5064
 
5.0%
m 5064
 
5.0%
r 5064
 
5.0%
Other values (4) 20256
20.0%
None
ValueCountFrequency (%)
ú 5064
100.0%

13-nombre_del_colegio
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing17181
Missing (%)21.9%
Memory size613.4 KiB

14-partido/depto
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing16982
Missing (%)21.6%
Memory size613.4 KiB

14-país
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing15279
Missing (%)19.5%
Memory size613.4 KiB

14-provincia
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing15397
Missing (%)19.6%
Memory size613.4 KiB

15-Orientación del título de Nivel Medio o Polimodal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing15354
Missing (%)19.6%
Memory size613.4 KiB

16-descripción
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing19300
Missing (%)24.6%
Memory size613.4 KiB

17-¿Tenés Obra Social y/o Mutual?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing14437
Missing (%)18.4%
Memory size613.4 KiB
17-si
34384 
17-no
29676 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters320300
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row17-si
2nd row17-no
3rd row17-si
4th row17-si
5th row17-no

Common Values

ValueCountFrequency (%)
17-si 34384
43.8%
17-no 29676
37.8%
(Missing) 14437
18.4%

Length

2023-10-29T11:55:02.470446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:02.573012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
17-si 34384
53.7%
17-no 29676
46.3%

Most occurring characters

ValueCountFrequency (%)
1 64060
20.0%
7 64060
20.0%
- 64060
20.0%
s 34384
10.7%
i 34384
10.7%
n 29676
9.3%
o 29676
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128120
40.0%
Lowercase Letter 128120
40.0%
Dash Punctuation 64060
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 34384
26.8%
i 34384
26.8%
n 29676
23.2%
o 29676
23.2%
Decimal Number
ValueCountFrequency (%)
1 64060
50.0%
7 64060
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 64060
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192180
60.0%
Latin 128120
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 34384
26.8%
i 34384
26.8%
n 29676
23.2%
o 29676
23.2%
Common
ValueCountFrequency (%)
1 64060
33.3%
7 64060
33.3%
- 64060
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64060
20.0%
7 64060
20.0%
- 64060
20.0%
s 34384
10.7%
i 34384
10.7%
n 29676
9.3%
o 29676
9.3%

18-¿Trabajás actualmente?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing13935
Missing (%)17.8%
Memory size613.4 KiB
18-si
40003 
18-no
24559 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters322810
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18-si
2nd row18-si
3rd row18-no
4th row18-si
5th row18-si

Common Values

ValueCountFrequency (%)
18-si 40003
51.0%
18-no 24559
31.3%
(Missing) 13935
 
17.8%

Length

2023-10-29T11:55:02.656221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:02.753249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
18-si 40003
62.0%
18-no 24559
38.0%

Most occurring characters

ValueCountFrequency (%)
1 64562
20.0%
8 64562
20.0%
- 64562
20.0%
s 40003
12.4%
i 40003
12.4%
n 24559
 
7.6%
o 24559
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 129124
40.0%
Lowercase Letter 129124
40.0%
Dash Punctuation 64562
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 40003
31.0%
i 40003
31.0%
n 24559
19.0%
o 24559
19.0%
Decimal Number
ValueCountFrequency (%)
1 64562
50.0%
8 64562
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 64562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193686
60.0%
Latin 129124
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 40003
31.0%
i 40003
31.0%
n 24559
19.0%
o 24559
19.0%
Common
ValueCountFrequency (%)
1 64562
33.3%
8 64562
33.3%
- 64562
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 322810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64562
20.0%
8 64562
20.0%
- 64562
20.0%
s 40003
12.4%
i 40003
12.4%
n 24559
 
7.6%
o 24559
 
7.6%

19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es:
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing14347
Missing (%)18.3%
Memory size613.4 KiB
19-no
34030 
19-si
30120 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters320750
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row19-si
2nd row19-si
3rd row19-si
4th row19-si
5th row19-si

Common Values

ValueCountFrequency (%)
19-no 34030
43.4%
19-si 30120
38.4%
(Missing) 14347
18.3%

Length

2023-10-29T11:55:02.833590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:02.930705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
19-no 34030
53.0%
19-si 30120
47.0%

Most occurring characters

ValueCountFrequency (%)
1 64150
20.0%
9 64150
20.0%
- 64150
20.0%
n 34030
10.6%
o 34030
10.6%
s 30120
9.4%
i 30120
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128300
40.0%
Lowercase Letter 128300
40.0%
Dash Punctuation 64150
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 34030
26.5%
o 34030
26.5%
s 30120
23.5%
i 30120
23.5%
Decimal Number
ValueCountFrequency (%)
1 64150
50.0%
9 64150
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 64150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 192450
60.0%
Latin 128300
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 34030
26.5%
o 34030
26.5%
s 30120
23.5%
i 30120
23.5%
Common
ValueCountFrequency (%)
1 64150
33.3%
9 64150
33.3%
- 64150
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 320750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64150
20.0%
9 64150
20.0%
- 64150
20.0%
n 34030
10.6%
o 34030
10.6%
s 30120
9.4%
i 30120
9.4%

20-En tu trabajo principal sos...
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing48424
Missing (%)61.7%
Memory size613.4 KiB
20-parcial
23216 
20-total
6661 
20-ninguna
 
196

Length

Max length10
Median length10
Mean length9.5570113
Min length8

Characters and Unicode

Total characters287408
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-parcial
2nd row20-parcial
3rd row20-parcial
4th row20-parcial
5th row20-parcial

Common Values

ValueCountFrequency (%)
20-parcial 23216
29.6%
20-total 6661
 
8.5%
20-ninguna 196
 
0.2%
(Missing) 48424
61.7%

Length

2023-10-29T11:55:03.012238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:03.106843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
20-parcial 23216
77.2%
20-total 6661
 
22.1%
20-ninguna 196
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 53289
18.5%
2 30073
10.5%
0 30073
10.5%
- 30073
10.5%
l 29877
10.4%
i 23412
8.1%
p 23216
8.1%
r 23216
8.1%
c 23216
8.1%
t 13322
 
4.6%
Other values (4) 7641
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197189
68.6%
Decimal Number 60146
 
20.9%
Dash Punctuation 30073
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53289
27.0%
l 29877
15.2%
i 23412
11.9%
p 23216
11.8%
r 23216
11.8%
c 23216
11.8%
t 13322
 
6.8%
o 6661
 
3.4%
n 588
 
0.3%
g 196
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 30073
50.0%
0 30073
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 30073
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 197189
68.6%
Common 90219
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53289
27.0%
l 29877
15.2%
i 23412
11.9%
p 23216
11.8%
r 23216
11.8%
c 23216
11.8%
t 13322
 
6.8%
o 6661
 
3.4%
n 588
 
0.3%
g 196
 
0.1%
Common
ValueCountFrequency (%)
2 30073
33.3%
0 30073
33.3%
- 30073
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 53289
18.5%
2 30073
10.5%
0 30073
10.5%
- 30073
10.5%
l 29877
10.4%
i 23412
8.1%
p 23216
8.1%
r 23216
8.1%
c 23216
8.1%
t 13322
 
4.6%
Other values (4) 7641
 
2.7%

21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal?
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing49632
Missing (%)63.2%
Memory size613.4 KiB
21-obrero_o_empleado
20979 
21-trabajador_familiar
4215 
21-cuenta_propista
2152 
21-patrón_o_encargado
 
1519

Length

Max length22
Median length20
Mean length20.195566
Min length18

Characters and Unicode

Total characters582945
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21-obrero_o_empleado
2nd row21-trabajador_familiar
3rd row21-obrero_o_empleado
4th row21-obrero_o_empleado
5th row21-obrero_o_empleado

Common Values

ValueCountFrequency (%)
21-obrero_o_empleado 20979
26.7%
21-trabajador_familiar 4215
 
5.4%
21-cuenta_propista 2152
 
2.7%
21-patrón_o_encargado 1519
 
1.9%
(Missing) 49632
63.2%

Length

2023-10-29T11:55:03.202400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:03.324909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
21-obrero_o_empleado 20979
72.7%
21-trabajador_familiar 4215
 
14.6%
21-cuenta_propista 2152
 
7.5%
21-patrón_o_encargado 1519
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o 93321
16.0%
e 66608
11.4%
r 59793
10.3%
_ 51363
8.8%
a 50915
8.7%
1 28865
 
5.0%
2 28865
 
5.0%
- 28865
 
5.0%
p 26802
 
4.6%
d 26713
 
4.6%
Other values (13) 120835
20.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 444987
76.3%
Decimal Number 57730
 
9.9%
Connector Punctuation 51363
 
8.8%
Dash Punctuation 28865
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 93321
21.0%
e 66608
15.0%
r 59793
13.4%
a 50915
11.4%
p 26802
 
6.0%
d 26713
 
6.0%
b 25194
 
5.7%
m 25194
 
5.7%
l 25194
 
5.7%
i 10582
 
2.4%
Other values (9) 34671
 
7.8%
Decimal Number
ValueCountFrequency (%)
1 28865
50.0%
2 28865
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 51363
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 444987
76.3%
Common 137958
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 93321
21.0%
e 66608
15.0%
r 59793
13.4%
a 50915
11.4%
p 26802
 
6.0%
d 26713
 
6.0%
b 25194
 
5.7%
m 25194
 
5.7%
l 25194
 
5.7%
i 10582
 
2.4%
Other values (9) 34671
 
7.8%
Common
ValueCountFrequency (%)
_ 51363
37.2%
1 28865
20.9%
2 28865
20.9%
- 28865
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 581426
99.7%
None 1519
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 93321
16.1%
e 66608
11.5%
r 59793
10.3%
_ 51363
8.8%
a 50915
8.8%
1 28865
 
5.0%
2 28865
 
5.0%
- 28865
 
5.0%
p 26802
 
4.6%
d 26713
 
4.6%
Other values (12) 119316
20.5%
None
ValueCountFrequency (%)
ó 1519
100.0%

22-¿Tu trabajo principal es?
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing49649
Missing (%)63.2%
Memory size613.4 KiB

23-¿Tu trabajo principal es?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing48965
Missing (%)62.4%
Memory size613.4 KiB
23-formal
15990 
23-informal
13542 

Length

Max length11
Median length9
Mean length9.9171069
Min length9

Characters and Unicode

Total characters292872
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row23-formal
2nd row23-informal
3rd row23-informal
4th row23-formal
5th row23-formal

Common Values

ValueCountFrequency (%)
23-formal 15990
 
20.4%
23-informal 13542
 
17.3%
(Missing) 48965
62.4%

Length

2023-10-29T11:55:03.421425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:03.562276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
23-formal 15990
54.1%
23-informal 13542
45.9%

Most occurring characters

ValueCountFrequency (%)
2 29532
10.1%
3 29532
10.1%
- 29532
10.1%
f 29532
10.1%
o 29532
10.1%
r 29532
10.1%
m 29532
10.1%
a 29532
10.1%
l 29532
10.1%
i 13542
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 204276
69.7%
Decimal Number 59064
 
20.2%
Dash Punctuation 29532
 
10.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 29532
14.5%
o 29532
14.5%
r 29532
14.5%
m 29532
14.5%
a 29532
14.5%
l 29532
14.5%
i 13542
6.6%
n 13542
6.6%
Decimal Number
ValueCountFrequency (%)
2 29532
50.0%
3 29532
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 29532
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 204276
69.7%
Common 88596
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 29532
14.5%
o 29532
14.5%
r 29532
14.5%
m 29532
14.5%
a 29532
14.5%
l 29532
14.5%
i 13542
6.6%
n 13542
6.6%
Common
ValueCountFrequency (%)
2 29532
33.3%
3 29532
33.3%
- 29532
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 292872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29532
10.1%
3 29532
10.1%
- 29532
10.1%
f 29532
10.1%
o 29532
10.1%
r 29532
10.1%
m 29532
10.1%
a 29532
10.1%
l 29532
10.1%
i 13542
4.6%

24-En una semana normal de trabajo ¿cuántas horas trabajás?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing49265
Missing (%)62.8%
Memory size613.4 KiB
24-permanente
16018 
24-temporario
13214 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters380016
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24-permanente
2nd row24-temporario
3rd row24-permanente
4th row24-permanente
5th row24-permanente

Common Values

ValueCountFrequency (%)
24-permanente 16018
 
20.4%
24-temporario 13214
 
16.8%
(Missing) 49265
62.8%

Length

2023-10-29T11:55:03.659293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:03.784358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
24-permanente 16018
54.8%
24-temporario 13214
45.2%

Most occurring characters

ValueCountFrequency (%)
e 61268
16.1%
r 42446
11.2%
n 32036
8.4%
2 29232
7.7%
4 29232
7.7%
- 29232
7.7%
p 29232
7.7%
m 29232
7.7%
a 29232
7.7%
t 29232
7.7%
Other values (2) 39642
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292320
76.9%
Decimal Number 58464
 
15.4%
Dash Punctuation 29232
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 61268
21.0%
r 42446
14.5%
n 32036
11.0%
p 29232
10.0%
m 29232
10.0%
a 29232
10.0%
t 29232
10.0%
o 26428
9.0%
i 13214
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 29232
50.0%
4 29232
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 29232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 292320
76.9%
Common 87696
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 61268
21.0%
r 42446
14.5%
n 32036
11.0%
p 29232
10.0%
m 29232
10.0%
a 29232
10.0%
t 29232
10.0%
o 26428
9.0%
i 13214
 
4.5%
Common
ValueCountFrequency (%)
2 29232
33.3%
4 29232
33.3%
- 29232
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 380016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 61268
16.1%
r 42446
11.2%
n 32036
8.4%
2 29232
7.7%
4 29232
7.7%
- 29232
7.7%
p 29232
7.7%
m 29232
7.7%
a 29232
7.7%
t 29232
7.7%
Other values (2) 39642
10.4%

25-Tus horarios son…
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing48540
Missing (%)61.8%
Memory size613.4 KiB
25-de_25_a_48_horas
14101 
25-de_11_a_24_horas
6767 
25-menos_de_10_horas
5516 
25-más_de_48_horas
3573 

Length

Max length20
Median length19
Mean length19.06486
Min length18

Characters and Unicode

Total characters571126
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-de_25_a_48_horas
2nd row25-de_25_a_48_horas
3rd row25-de_11_a_24_horas
4th row25-de_25_a_48_horas
5th row25-más_de_48_horas

Common Values

ValueCountFrequency (%)
25-de_25_a_48_horas 14101
 
18.0%
25-de_11_a_24_horas 6767
 
8.6%
25-menos_de_10_horas 5516
 
7.0%
25-más_de_48_horas 3573
 
4.6%
(Missing) 48540
61.8%

Length

2023-10-29T11:55:03.888884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:04.004260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
25-de_25_a_48_horas 14101
47.1%
25-de_11_a_24_horas 6767
22.6%
25-menos_de_10_horas 5516
 
18.4%
25-más_de_48_horas 3573
 
11.9%

Most occurring characters

ValueCountFrequency (%)
_ 110739
19.4%
2 50825
8.9%
a 50825
8.9%
5 44058
 
7.7%
s 39046
 
6.8%
e 35473
 
6.2%
o 35473
 
6.2%
r 29957
 
5.2%
h 29957
 
5.2%
d 29957
 
5.2%
Other values (8) 114816
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 268866
47.1%
Decimal Number 161564
28.3%
Connector Punctuation 110739
19.4%
Dash Punctuation 29957
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50825
18.9%
s 39046
14.5%
e 35473
13.2%
o 35473
13.2%
r 29957
11.1%
h 29957
11.1%
d 29957
11.1%
m 9089
 
3.4%
n 5516
 
2.1%
á 3573
 
1.3%
Decimal Number
ValueCountFrequency (%)
2 50825
31.5%
5 44058
27.3%
4 24441
15.1%
1 19050
 
11.8%
8 17674
 
10.9%
0 5516
 
3.4%
Connector Punctuation
ValueCountFrequency (%)
_ 110739
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29957
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302260
52.9%
Latin 268866
47.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50825
18.9%
s 39046
14.5%
e 35473
13.2%
o 35473
13.2%
r 29957
11.1%
h 29957
11.1%
d 29957
11.1%
m 9089
 
3.4%
n 5516
 
2.1%
á 3573
 
1.3%
Common
ValueCountFrequency (%)
_ 110739
36.6%
2 50825
16.8%
5 44058
 
14.6%
- 29957
 
9.9%
4 24441
 
8.1%
1 19050
 
6.3%
8 17674
 
5.8%
0 5516
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 567553
99.4%
None 3573
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 110739
19.5%
2 50825
9.0%
a 50825
9.0%
5 44058
 
7.8%
s 39046
 
6.9%
e 35473
 
6.3%
o 35473
 
6.3%
r 29957
 
5.3%
h 29957
 
5.3%
d 29957
 
5.3%
Other values (7) 111243
19.6%
None
ValueCountFrequency (%)
á 3573
100.0%

26-¿Qué momentos del día abarca tu jornada de trabajo?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing49063
Missing (%)62.5%
Memory size613.4 KiB
26-fijos
19951 
26-rotativos
9483 

Length

Max length12
Median length8
Mean length9.2887137
Min length8

Characters and Unicode

Total characters273404
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26-fijos
2nd row26-fijos
3rd row26-fijos
4th row26-fijos
5th row26-fijos

Common Values

ValueCountFrequency (%)
26-fijos 19951
25.4%
26-rotativos 9483
 
12.1%
(Missing) 49063
62.5%

Length

2023-10-29T11:55:04.132790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:04.267369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
26-fijos 19951
67.8%
26-rotativos 9483
32.2%

Most occurring characters

ValueCountFrequency (%)
o 38917
14.2%
2 29434
10.8%
6 29434
10.8%
- 29434
10.8%
i 29434
10.8%
s 29434
10.8%
f 19951
7.3%
j 19951
7.3%
t 18966
6.9%
r 9483
 
3.5%
Other values (2) 18966
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185102
67.7%
Decimal Number 58868
 
21.5%
Dash Punctuation 29434
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 38917
21.0%
i 29434
15.9%
s 29434
15.9%
f 19951
10.8%
j 19951
10.8%
t 18966
10.2%
r 9483
 
5.1%
a 9483
 
5.1%
v 9483
 
5.1%
Decimal Number
ValueCountFrequency (%)
2 29434
50.0%
6 29434
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 29434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185102
67.7%
Common 88302
32.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 38917
21.0%
i 29434
15.9%
s 29434
15.9%
f 19951
10.8%
j 19951
10.8%
t 18966
10.2%
r 9483
 
5.1%
a 9483
 
5.1%
v 9483
 
5.1%
Common
ValueCountFrequency (%)
2 29434
33.3%
6 29434
33.3%
- 29434
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 38917
14.2%
2 29434
10.8%
6 29434
10.8%
- 29434
10.8%
i 29434
10.8%
s 29434
10.8%
f 19951
7.3%
j 19951
7.3%
t 18966
6.9%
r 9483
 
3.5%
Other values (2) 18966
6.9%

27-¿Podés cambiar los horarios de trabajo?
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing48986
Missing (%)62.4%
Memory size613.4 KiB
27-mañana
14335 
27-tarde
12150 
27-noche
3026 

Length

Max length9
Median length8
Mean length8.4857511
Min length8

Characters and Unicode

Total characters250423
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27-mañana
2nd row27-mañana
3rd row27-mañana
4th row27-mañana
5th row27-mañana

Common Values

ValueCountFrequency (%)
27-mañana 14335
 
18.3%
27-tarde 12150
 
15.5%
27-noche 3026
 
3.9%
(Missing) 48986
62.4%

Length

2023-10-29T11:55:04.365896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:04.528529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
27-mañana 14335
48.6%
27-tarde 12150
41.2%
27-noche 3026
 
10.3%

Most occurring characters

ValueCountFrequency (%)
a 55155
22.0%
2 29511
11.8%
7 29511
11.8%
- 29511
11.8%
n 17361
 
6.9%
e 15176
 
6.1%
m 14335
 
5.7%
ñ 14335
 
5.7%
t 12150
 
4.9%
r 12150
 
4.9%
Other values (4) 21228
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 161890
64.6%
Decimal Number 59022
 
23.6%
Dash Punctuation 29511
 
11.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55155
34.1%
n 17361
 
10.7%
e 15176
 
9.4%
m 14335
 
8.9%
ñ 14335
 
8.9%
t 12150
 
7.5%
r 12150
 
7.5%
d 12150
 
7.5%
o 3026
 
1.9%
c 3026
 
1.9%
Decimal Number
ValueCountFrequency (%)
2 29511
50.0%
7 29511
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 29511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161890
64.6%
Common 88533
35.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55155
34.1%
n 17361
 
10.7%
e 15176
 
9.4%
m 14335
 
8.9%
ñ 14335
 
8.9%
t 12150
 
7.5%
r 12150
 
7.5%
d 12150
 
7.5%
o 3026
 
1.9%
c 3026
 
1.9%
Common
ValueCountFrequency (%)
2 29511
33.3%
7 29511
33.3%
- 29511
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236088
94.3%
None 14335
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55155
23.4%
2 29511
12.5%
7 29511
12.5%
- 29511
12.5%
n 17361
 
7.4%
e 15176
 
6.4%
m 14335
 
6.1%
t 12150
 
5.1%
r 12150
 
5.1%
d 12150
 
5.1%
Other values (3) 9078
 
3.8%
None
ValueCountFrequency (%)
ñ 14335
100.0%

28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan)
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing48081
Missing (%)61.3%
Memory size613.4 KiB
28-sólo_algunas_veces
11658 
28-siempre_que_sea_necesario
9656 
28-nunca
9102 

Length

Max length28
Median length21
Mean length19.331996
Min length8

Characters and Unicode

Total characters588002
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28-sólo_algunas_veces
2nd row28-nunca
3rd row28-nunca
4th row28-sólo_algunas_veces
5th row28-nunca

Common Values

ValueCountFrequency (%)
28-sólo_algunas_veces 11658
 
14.9%
28-siempre_que_sea_necesario 9656
 
12.3%
28-nunca 9102
 
11.6%
(Missing) 48081
61.3%

Length

2023-10-29T11:55:04.665114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:04.796152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
28-sólo_algunas_veces 11658
38.3%
28-siempre_que_sea_necesario 9656
31.7%
28-nunca 9102
29.9%

Most occurring characters

ValueCountFrequency (%)
e 81252
13.8%
s 63942
10.9%
_ 52284
 
8.9%
a 51730
 
8.8%
n 39518
 
6.7%
2 30416
 
5.2%
c 30416
 
5.2%
8 30416
 
5.2%
u 30416
 
5.2%
- 30416
 
5.2%
Other values (10) 147196
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 444470
75.6%
Decimal Number 60832
 
10.3%
Connector Punctuation 52284
 
8.9%
Dash Punctuation 30416
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 81252
18.3%
s 63942
14.4%
a 51730
11.6%
n 39518
8.9%
c 30416
 
6.8%
u 30416
 
6.8%
l 23316
 
5.2%
o 21314
 
4.8%
i 19312
 
4.3%
r 19312
 
4.3%
Other values (6) 63942
14.4%
Decimal Number
ValueCountFrequency (%)
2 30416
50.0%
8 30416
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 444470
75.6%
Common 143532
 
24.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 81252
18.3%
s 63942
14.4%
a 51730
11.6%
n 39518
8.9%
c 30416
 
6.8%
u 30416
 
6.8%
l 23316
 
5.2%
o 21314
 
4.8%
i 19312
 
4.3%
r 19312
 
4.3%
Other values (6) 63942
14.4%
Common
ValueCountFrequency (%)
_ 52284
36.4%
2 30416
21.2%
8 30416
21.2%
- 30416
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576344
98.0%
None 11658
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 81252
14.1%
s 63942
11.1%
_ 52284
 
9.1%
a 51730
 
9.0%
n 39518
 
6.9%
2 30416
 
5.3%
c 30416
 
5.3%
8 30416
 
5.3%
u 30416
 
5.3%
- 30416
 
5.3%
Other values (9) 135538
23.5%
None
ValueCountFrequency (%)
ó 11658
100.0%

29-no
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing67247
Missing (%)85.7%
Memory size613.4 KiB
29-no
11250 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters56250
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29-no
2nd row29-no
3rd row29-no
4th row29-no
5th row29-no

Common Values

ValueCountFrequency (%)
29-no 11250
 
14.3%
(Missing) 67247
85.7%

Length

2023-10-29T11:55:04.932671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:05.032418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
29-no 11250
100.0%

Most occurring characters

ValueCountFrequency (%)
2 11250
20.0%
9 11250
20.0%
- 11250
20.0%
n 11250
20.0%
o 11250
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22500
40.0%
Lowercase Letter 22500
40.0%
Dash Punctuation 11250
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11250
50.0%
9 11250
50.0%
Lowercase Letter
ValueCountFrequency (%)
n 11250
50.0%
o 11250
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 11250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33750
60.0%
Latin 22500
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11250
33.3%
9 11250
33.3%
- 11250
33.3%
Latin
ValueCountFrequency (%)
n 11250
50.0%
o 11250
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11250
20.0%
9 11250
20.0%
- 11250
20.0%
n 11250
20.0%
o 11250
20.0%

29-si
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing59040
Missing (%)75.2%
Memory size613.4 KiB
29-si
19457 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters97285
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29-si
2nd row29-si
3rd row29-si
4th row29-si
5th row29-si

Common Values

ValueCountFrequency (%)
29-si 19457
 
24.8%
(Missing) 59040
75.2%

Length

2023-10-29T11:55:05.189842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:05.522367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
29-si 19457
100.0%

Most occurring characters

ValueCountFrequency (%)
2 19457
20.0%
9 19457
20.0%
- 19457
20.0%
s 19457
20.0%
i 19457
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38914
40.0%
Lowercase Letter 38914
40.0%
Dash Punctuation 19457
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19457
50.0%
9 19457
50.0%
Lowercase Letter
ValueCountFrequency (%)
s 19457
50.0%
i 19457
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 19457
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58371
60.0%
Latin 38914
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 19457
33.3%
9 19457
33.3%
- 19457
33.3%
Latin
ValueCountFrequency (%)
s 19457
50.0%
i 19457
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19457
20.0%
9 19457
20.0%
- 19457
20.0%
s 19457
20.0%
i 19457
20.0%

30-abuelas/os
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing72692
Missing (%)92.6%
Memory size613.4 KiB
30-abuelas/os
5805 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters75465
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-abuelas/os
2nd row30-abuelas/os
3rd row30-abuelas/os
4th row30-abuelas/os
5th row30-abuelas/os

Common Values

ValueCountFrequency (%)
30-abuelas/os 5805
 
7.4%
(Missing) 72692
92.6%

Length

2023-10-29T11:55:05.789173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:06.124392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-abuelas/os 5805
100.0%

Most occurring characters

ValueCountFrequency (%)
a 11610
15.4%
s 11610
15.4%
3 5805
7.7%
0 5805
7.7%
- 5805
7.7%
b 5805
7.7%
u 5805
7.7%
e 5805
7.7%
l 5805
7.7%
/ 5805
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52245
69.2%
Decimal Number 11610
 
15.4%
Dash Punctuation 5805
 
7.7%
Other Punctuation 5805
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11610
22.2%
s 11610
22.2%
b 5805
11.1%
u 5805
11.1%
e 5805
11.1%
l 5805
11.1%
o 5805
11.1%
Decimal Number
ValueCountFrequency (%)
3 5805
50.0%
0 5805
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 5805
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 5805
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52245
69.2%
Common 23220
30.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11610
22.2%
s 11610
22.2%
b 5805
11.1%
u 5805
11.1%
e 5805
11.1%
l 5805
11.1%
o 5805
11.1%
Common
ValueCountFrequency (%)
3 5805
25.0%
0 5805
25.0%
- 5805
25.0%
/ 5805
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11610
15.4%
s 11610
15.4%
3 5805
7.7%
0 5805
7.7%
- 5805
7.7%
b 5805
7.7%
u 5805
7.7%
e 5805
7.7%
l 5805
7.7%
/ 5805
7.7%

30-amigas/os
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing77884
Missing (%)99.2%
Memory size613.4 KiB
30-amigas/os
613 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters7356
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-amigas/os
2nd row30-amigas/os
3rd row30-amigas/os
4th row30-amigas/os
5th row30-amigas/os

Common Values

ValueCountFrequency (%)
30-amigas/os 613
 
0.8%
(Missing) 77884
99.2%

Length

2023-10-29T11:55:06.411281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:06.707212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-amigas/os 613
100.0%

Most occurring characters

ValueCountFrequency (%)
a 1226
16.7%
s 1226
16.7%
3 613
8.3%
0 613
8.3%
- 613
8.3%
m 613
8.3%
i 613
8.3%
g 613
8.3%
/ 613
8.3%
o 613
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4904
66.7%
Decimal Number 1226
 
16.7%
Dash Punctuation 613
 
8.3%
Other Punctuation 613
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1226
25.0%
s 1226
25.0%
m 613
12.5%
i 613
12.5%
g 613
12.5%
o 613
12.5%
Decimal Number
ValueCountFrequency (%)
3 613
50.0%
0 613
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 613
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 613
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4904
66.7%
Common 2452
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1226
25.0%
s 1226
25.0%
m 613
12.5%
i 613
12.5%
g 613
12.5%
o 613
12.5%
Common
ValueCountFrequency (%)
3 613
25.0%
0 613
25.0%
- 613
25.0%
/ 613
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1226
16.7%
s 1226
16.7%
3 613
8.3%
0 613
8.3%
- 613
8.3%
m 613
8.3%
i 613
8.3%
g 613
8.3%
/ 613
8.3%
o 613
8.3%

30-esposa/o_o_pareja
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing66897
Missing (%)85.2%
Memory size613.4 KiB
30-esposa/o_o_pareja
11600 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters232000
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-esposa/o_o_pareja
2nd row30-esposa/o_o_pareja
3rd row30-esposa/o_o_pareja
4th row30-esposa/o_o_pareja
5th row30-esposa/o_o_pareja

Common Values

ValueCountFrequency (%)
30-esposa/o_o_pareja 11600
 
14.8%
(Missing) 66897
85.2%

Length

2023-10-29T11:55:06.968905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:07.250887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-esposa/o_o_pareja 11600
100.0%

Most occurring characters

ValueCountFrequency (%)
o 34800
15.0%
a 34800
15.0%
e 23200
10.0%
s 23200
10.0%
p 23200
10.0%
_ 23200
10.0%
3 11600
 
5.0%
0 11600
 
5.0%
- 11600
 
5.0%
/ 11600
 
5.0%
Other values (2) 23200
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162400
70.0%
Connector Punctuation 23200
 
10.0%
Decimal Number 23200
 
10.0%
Dash Punctuation 11600
 
5.0%
Other Punctuation 11600
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 34800
21.4%
a 34800
21.4%
e 23200
14.3%
s 23200
14.3%
p 23200
14.3%
r 11600
 
7.1%
j 11600
 
7.1%
Decimal Number
ValueCountFrequency (%)
3 11600
50.0%
0 11600
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11600
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 11600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162400
70.0%
Common 69600
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 34800
21.4%
a 34800
21.4%
e 23200
14.3%
s 23200
14.3%
p 23200
14.3%
r 11600
 
7.1%
j 11600
 
7.1%
Common
ValueCountFrequency (%)
_ 23200
33.3%
3 11600
16.7%
0 11600
16.7%
- 11600
16.7%
/ 11600
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 232000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 34800
15.0%
a 34800
15.0%
e 23200
10.0%
s 23200
10.0%
p 23200
10.0%
_ 23200
10.0%
3 11600
 
5.0%
0 11600
 
5.0%
- 11600
 
5.0%
/ 11600
 
5.0%
Other values (2) 23200
10.0%

30-hermanas/os
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing39849
Missing (%)50.8%
Memory size613.4 KiB
30-hermanas/os
38648 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters541072
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-hermanas/os
2nd row30-hermanas/os
3rd row30-hermanas/os
4th row30-hermanas/os
5th row30-hermanas/os

Common Values

ValueCountFrequency (%)
30-hermanas/os 38648
49.2%
(Missing) 39849
50.8%

Length

2023-10-29T11:55:07.442020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:07.667813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-hermanas/os 38648
100.0%

Most occurring characters

ValueCountFrequency (%)
a 77296
14.3%
s 77296
14.3%
3 38648
7.1%
0 38648
7.1%
- 38648
7.1%
h 38648
7.1%
e 38648
7.1%
r 38648
7.1%
m 38648
7.1%
n 38648
7.1%
Other values (2) 77296
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 386480
71.4%
Decimal Number 77296
 
14.3%
Dash Punctuation 38648
 
7.1%
Other Punctuation 38648
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 77296
20.0%
s 77296
20.0%
h 38648
10.0%
e 38648
10.0%
r 38648
10.0%
m 38648
10.0%
n 38648
10.0%
o 38648
10.0%
Decimal Number
ValueCountFrequency (%)
3 38648
50.0%
0 38648
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 38648
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 38648
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 386480
71.4%
Common 154592
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 77296
20.0%
s 77296
20.0%
h 38648
10.0%
e 38648
10.0%
r 38648
10.0%
m 38648
10.0%
n 38648
10.0%
o 38648
10.0%
Common
ValueCountFrequency (%)
3 38648
25.0%
0 38648
25.0%
- 38648
25.0%
/ 38648
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 541072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 77296
14.3%
s 77296
14.3%
3 38648
7.1%
0 38648
7.1%
- 38648
7.1%
h 38648
7.1%
e 38648
7.1%
r 38648
7.1%
m 38648
7.1%
n 38648
7.1%
Other values (2) 77296
14.3%

30-hijas/os
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing67558
Missing (%)86.1%
Memory size613.4 KiB
30-hijas/os
10939 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters120329
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-hijas/os
2nd row30-hijas/os
3rd row30-hijas/os
4th row30-hijas/os
5th row30-hijas/os

Common Values

ValueCountFrequency (%)
30-hijas/os 10939
 
13.9%
(Missing) 67558
86.1%

Length

2023-10-29T11:55:07.854746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:08.060575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-hijas/os 10939
100.0%

Most occurring characters

ValueCountFrequency (%)
s 21878
18.2%
3 10939
9.1%
0 10939
9.1%
- 10939
9.1%
h 10939
9.1%
i 10939
9.1%
j 10939
9.1%
a 10939
9.1%
/ 10939
9.1%
o 10939
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76573
63.6%
Decimal Number 21878
 
18.2%
Dash Punctuation 10939
 
9.1%
Other Punctuation 10939
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 21878
28.6%
h 10939
14.3%
i 10939
14.3%
j 10939
14.3%
a 10939
14.3%
o 10939
14.3%
Decimal Number
ValueCountFrequency (%)
3 10939
50.0%
0 10939
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 10939
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 10939
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 76573
63.6%
Common 43756
36.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 21878
28.6%
h 10939
14.3%
i 10939
14.3%
j 10939
14.3%
a 10939
14.3%
o 10939
14.3%
Common
ValueCountFrequency (%)
3 10939
25.0%
0 10939
25.0%
- 10939
25.0%
/ 10939
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 21878
18.2%
3 10939
9.1%
0 10939
9.1%
- 10939
9.1%
h 10939
9.1%
i 10939
9.1%
j 10939
9.1%
a 10939
9.1%
/ 10939
9.1%
o 10939
9.1%

30-madre
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing32798
Missing (%)41.8%
Memory size613.4 KiB
30-madre
45699 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters365592
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-madre
2nd row30-madre
3rd row30-madre
4th row30-madre
5th row30-madre

Common Values

ValueCountFrequency (%)
30-madre 45699
58.2%
(Missing) 32798
41.8%

Length

2023-10-29T11:55:08.254401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:08.565387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-madre 45699
100.0%

Most occurring characters

ValueCountFrequency (%)
3 45699
12.5%
0 45699
12.5%
- 45699
12.5%
m 45699
12.5%
a 45699
12.5%
d 45699
12.5%
r 45699
12.5%
e 45699
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228495
62.5%
Decimal Number 91398
 
25.0%
Dash Punctuation 45699
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 45699
20.0%
a 45699
20.0%
d 45699
20.0%
r 45699
20.0%
e 45699
20.0%
Decimal Number
ValueCountFrequency (%)
3 45699
50.0%
0 45699
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 45699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228495
62.5%
Common 137097
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 45699
20.0%
a 45699
20.0%
d 45699
20.0%
r 45699
20.0%
e 45699
20.0%
Common
ValueCountFrequency (%)
3 45699
33.3%
0 45699
33.3%
- 45699
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 45699
12.5%
0 45699
12.5%
- 45699
12.5%
m 45699
12.5%
a 45699
12.5%
d 45699
12.5%
r 45699
12.5%
e 45699
12.5%

30-otros_(especificar)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing73082
Missing (%)93.1%
Memory size613.4 KiB

30-padre
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing43374
Missing (%)55.3%
Memory size613.4 KiB
30-padre
35123 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters280984
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-padre
2nd row30-padre
3rd row30-padre
4th row30-padre
5th row30-padre

Common Values

ValueCountFrequency (%)
30-padre 35123
44.7%
(Missing) 43374
55.3%

Length

2023-10-29T11:55:08.802675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:09.086358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-padre 35123
100.0%

Most occurring characters

ValueCountFrequency (%)
3 35123
12.5%
0 35123
12.5%
- 35123
12.5%
p 35123
12.5%
a 35123
12.5%
d 35123
12.5%
r 35123
12.5%
e 35123
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175615
62.5%
Decimal Number 70246
 
25.0%
Dash Punctuation 35123
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 35123
20.0%
a 35123
20.0%
d 35123
20.0%
r 35123
20.0%
e 35123
20.0%
Decimal Number
ValueCountFrequency (%)
3 35123
50.0%
0 35123
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 35123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175615
62.5%
Common 105369
37.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 35123
20.0%
a 35123
20.0%
d 35123
20.0%
r 35123
20.0%
e 35123
20.0%
Common
ValueCountFrequency (%)
3 35123
33.3%
0 35123
33.3%
- 35123
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 280984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 35123
12.5%
0 35123
12.5%
- 35123
12.5%
p 35123
12.5%
a 35123
12.5%
d 35123
12.5%
r 35123
12.5%
e 35123
12.5%

30-sobrinas/os
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing75082
Missing (%)95.6%
Memory size613.4 KiB
30-sobrinas/os
3415 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters47810
Distinct characters11
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-sobrinas/os
2nd row30-sobrinas/os
3rd row30-sobrinas/os
4th row30-sobrinas/os
5th row30-sobrinas/os

Common Values

ValueCountFrequency (%)
30-sobrinas/os 3415
 
4.4%
(Missing) 75082
95.6%

Length

2023-10-29T11:55:09.269613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:09.486743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-sobrinas/os 3415
100.0%

Most occurring characters

ValueCountFrequency (%)
s 10245
21.4%
o 6830
14.3%
3 3415
 
7.1%
0 3415
 
7.1%
- 3415
 
7.1%
b 3415
 
7.1%
r 3415
 
7.1%
i 3415
 
7.1%
n 3415
 
7.1%
a 3415
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34150
71.4%
Decimal Number 6830
 
14.3%
Dash Punctuation 3415
 
7.1%
Other Punctuation 3415
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 10245
30.0%
o 6830
20.0%
b 3415
 
10.0%
r 3415
 
10.0%
i 3415
 
10.0%
n 3415
 
10.0%
a 3415
 
10.0%
Decimal Number
ValueCountFrequency (%)
3 3415
50.0%
0 3415
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 3415
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3415
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34150
71.4%
Common 13660
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 10245
30.0%
o 6830
20.0%
b 3415
 
10.0%
r 3415
 
10.0%
i 3415
 
10.0%
n 3415
 
10.0%
a 3415
 
10.0%
Common
ValueCountFrequency (%)
3 3415
25.0%
0 3415
25.0%
- 3415
25.0%
/ 3415
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 10245
21.4%
o 6830
14.3%
3 3415
 
7.1%
0 3415
 
7.1%
- 3415
 
7.1%
b 3415
 
7.1%
r 3415
 
7.1%
i 3415
 
7.1%
n 3415
 
7.1%
a 3415
 
7.1%

30-vivo_sola/o
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing76057
Missing (%)96.9%
Memory size613.4 KiB
30-vivo_sola/o
2440 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters34160
Distinct characters11
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30-vivo_sola/o
2nd row30-vivo_sola/o
3rd row30-vivo_sola/o
4th row30-vivo_sola/o
5th row30-vivo_sola/o

Common Values

ValueCountFrequency (%)
30-vivo_sola/o 2440
 
3.1%
(Missing) 76057
96.9%

Length

2023-10-29T11:55:09.665742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:09.926857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
30-vivo_sola/o 2440
100.0%

Most occurring characters

ValueCountFrequency (%)
o 7320
21.4%
v 4880
14.3%
3 2440
 
7.1%
0 2440
 
7.1%
- 2440
 
7.1%
i 2440
 
7.1%
_ 2440
 
7.1%
s 2440
 
7.1%
l 2440
 
7.1%
a 2440
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21960
64.3%
Decimal Number 4880
 
14.3%
Dash Punctuation 2440
 
7.1%
Connector Punctuation 2440
 
7.1%
Other Punctuation 2440
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7320
33.3%
v 4880
22.2%
i 2440
 
11.1%
s 2440
 
11.1%
l 2440
 
11.1%
a 2440
 
11.1%
Decimal Number
ValueCountFrequency (%)
3 2440
50.0%
0 2440
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2440
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2440
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21960
64.3%
Common 12200
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7320
33.3%
v 4880
22.2%
i 2440
 
11.1%
s 2440
 
11.1%
l 2440
 
11.1%
a 2440
 
11.1%
Common
ValueCountFrequency (%)
3 2440
20.0%
0 2440
20.0%
- 2440
20.0%
_ 2440
20.0%
/ 2440
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7320
21.4%
v 4880
14.3%
3 2440
 
7.1%
0 2440
 
7.1%
- 2440
 
7.1%
i 2440
 
7.1%
_ 2440
 
7.1%
s 2440
 
7.1%
l 2440
 
7.1%
a 2440
 
7.1%

31-especificar
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)0.1%
Missing17160
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean4.140421
Minimum0
Maximum60
Zeros344
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:10.230244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum60
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8885901
Coefficient of variation (CV)0.4561348
Kurtosis97.934755
Mean4.140421
Median Absolute Deviation (MAD)1
Skewness4.7158879
Sum253961
Variance3.5667724
MonotonicityNot monotonic
2023-10-29T11:55:10.700904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4 17294
22.0%
3 12449
15.9%
5 12027
15.3%
2 6766
 
8.6%
6 5719
 
7.3%
7 2402
 
3.1%
1 2164
 
2.8%
8 1112
 
1.4%
9 479
 
0.6%
0 344
 
0.4%
Other values (24) 581
 
0.7%
(Missing) 17160
21.9%
ValueCountFrequency (%)
0 344
 
0.4%
1 2164
 
2.8%
2 6766
 
8.6%
3 12449
15.9%
4 17294
22.0%
5 12027
15.3%
6 5719
 
7.3%
7 2402
 
3.1%
8 1112
 
1.4%
9 479
 
0.6%
ValueCountFrequency (%)
60 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
54 2
< 0.1%
52 3
< 0.1%
51 1
 
< 0.1%
43 1
 
< 0.1%
42 2
< 0.1%
41 3
< 0.1%
40 1
 
< 0.1%

32-medio_o_polimodal
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct31
Distinct (%)0.1%
Missing34773
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean1.8708947
Minimum0
Maximum1211
Zeros1110
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:11.220465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum1211
Range1211
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.9225656
Coefficient of variation (CV)3.1656328
Kurtosis39734.871
Mean1.8708947
Median Absolute Deviation (MAD)1
Skewness194.69054
Sum81803
Variance35.076783
MonotonicityNot monotonic
2023-10-29T11:55:11.628439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 19644
25.0%
2 14005
17.8%
3 5593
 
7.1%
4 2089
 
2.7%
0 1110
 
1.4%
5 782
 
1.0%
6 283
 
0.4%
7 93
 
0.1%
8 42
 
0.1%
9 19
 
< 0.1%
Other values (21) 64
 
0.1%
(Missing) 34773
44.3%
ValueCountFrequency (%)
0 1110
 
1.4%
1 19644
25.0%
2 14005
17.8%
3 5593
 
7.1%
4 2089
 
2.7%
5 782
 
1.0%
6 283
 
0.4%
7 93
 
0.1%
8 42
 
0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
1211 1
 
< 0.1%
57 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 2
< 0.1%
31 3
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
23 1
 
< 0.1%

32-primario_o_egb
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct26
Distinct (%)0.1%
Missing42505
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2.1992665
Minimum0
Maximum322
Zeros497
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:11.977597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum322
Range322
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.299435
Coefficient of variation (CV)1.0455463
Kurtosis10448.54
Mean2.1992665
Median Absolute Deviation (MAD)1
Skewness76.979728
Sum79156
Variance5.2874013
MonotonicityNot monotonic
2023-10-29T11:55:12.420295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 13319
 
17.0%
2 11219
 
14.3%
3 5735
 
7.3%
4 2792
 
3.6%
5 1327
 
1.7%
6 575
 
0.7%
0 497
 
0.6%
7 255
 
0.3%
8 116
 
0.1%
10 50
 
0.1%
Other values (16) 107
 
0.1%
(Missing) 42505
54.1%
ValueCountFrequency (%)
0 497
 
0.6%
1 13319
17.0%
2 11219
14.3%
3 5735
7.3%
4 2792
 
3.6%
5 1327
 
1.7%
6 575
 
0.7%
7 255
 
0.3%
8 116
 
0.1%
9 50
 
0.1%
ValueCountFrequency (%)
322 1
 
< 0.1%
85 1
 
< 0.1%
43 1
 
< 0.1%
32 2
 
< 0.1%
30 1
 
< 0.1%
23 2
 
< 0.1%
21 5
< 0.1%
20 5
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%

32-terciario_no_universitario
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)0.1%
Missing59193
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean1.120804
Minimum0
Maximum32
Zeros3709
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:12.763589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0187153
Coefficient of variation (CV)0.9089148
Kurtosis201.85546
Mean1.120804
Median Absolute Deviation (MAD)0
Skewness8.3849715
Sum21636
Variance1.0377809
MonotonicityNot monotonic
2023-10-29T11:55:13.059016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 11377
 
14.5%
0 3709
 
4.7%
2 3155
 
4.0%
3 735
 
0.9%
4 177
 
0.2%
5 91
 
0.1%
6 29
 
< 0.1%
7 10
 
< 0.1%
8 5
 
< 0.1%
30 3
 
< 0.1%
Other values (9) 13
 
< 0.1%
(Missing) 59193
75.4%
ValueCountFrequency (%)
0 3709
 
4.7%
1 11377
14.5%
2 3155
 
4.0%
3 735
 
0.9%
4 177
 
0.2%
5 91
 
0.1%
6 29
 
< 0.1%
7 10
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
32 1
 
< 0.1%
31 1
 
< 0.1%
30 3
< 0.1%
20 2
< 0.1%
14 1
 
< 0.1%
13 2
< 0.1%
12 2
< 0.1%
11 1
 
< 0.1%
10 2
< 0.1%
9 1
 
< 0.1%

32-universitarios
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.1%
Missing63050
Missing (%)80.3%
Infinite0
Infinite (%)0.0%
Mean0.96193436
Minimum0
Maximum30
Zeros4445
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:13.450733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92680518
Coefficient of variation (CV)0.9634807
Kurtosis109.15208
Mean0.96193436
Median Absolute Deviation (MAD)0
Skewness5.2156544
Sum14859
Variance0.85896785
MonotonicityNot monotonic
2023-10-29T11:55:13.845412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 8192
 
10.4%
0 4445
 
5.7%
2 2173
 
2.8%
3 424
 
0.5%
4 141
 
0.2%
5 43
 
0.1%
6 16
 
< 0.1%
10 4
 
< 0.1%
7 2
 
< 0.1%
11 1
 
< 0.1%
Other values (6) 6
 
< 0.1%
(Missing) 63050
80.3%
ValueCountFrequency (%)
0 4445
5.7%
1 8192
10.4%
2 2173
 
2.8%
3 424
 
0.5%
4 141
 
0.2%
5 43
 
0.1%
6 16
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
23 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 16
< 0.1%

33-no_trabajan_pero_estan_buscando_trabajo
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)0.1%
Missing69840
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean0.9502137
Minimum0
Maximum50
Zeros2418
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:14.333751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98495745
Coefficient of variation (CV)1.0365641
Kurtosis720.15745
Mean0.9502137
Median Absolute Deviation (MAD)0
Skewness15.637859
Sum8226
Variance0.97014119
MonotonicityNot monotonic
2023-10-29T11:55:14.664123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 4777
 
6.1%
0 2418
 
3.1%
2 1136
 
1.4%
3 240
 
0.3%
4 59
 
0.1%
5 13
 
< 0.1%
6 5
 
< 0.1%
7 3
 
< 0.1%
10 3
 
< 0.1%
50 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 69840
89.0%
ValueCountFrequency (%)
0 2418
3.1%
1 4777
6.1%
2 1136
 
1.4%
3 240
 
0.3%
4 59
 
0.1%
5 13
 
< 0.1%
6 5
 
< 0.1%
7 3
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
16 1
 
< 0.1%
10 3
 
< 0.1%
9 1
 
< 0.1%
7 3
 
< 0.1%
6 5
 
< 0.1%
5 13
 
< 0.1%
4 59
 
0.1%
3 240
 
0.3%
2 1136
1.4%

33-no_trabajan_porque_estudian
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)0.1%
Missing60452
Missing (%)77.0%
Infinite0
Infinite (%)0.0%
Mean1.2106401
Minimum0
Maximum50
Zeros3450
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:14.938277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum50
Range50
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.052658
Coefficient of variation (CV)0.86950537
Kurtosis288.6083
Mean1.2106401
Median Absolute Deviation (MAD)0
Skewness8.0871665
Sum21846
Variance1.108089
MonotonicityNot monotonic
2023-10-29T11:55:15.171732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 9587
 
12.2%
2 3511
 
4.5%
0 3450
 
4.4%
3 1072
 
1.4%
4 290
 
0.4%
5 88
 
0.1%
6 28
 
< 0.1%
8 5
 
< 0.1%
7 4
 
< 0.1%
10 2
 
< 0.1%
Other values (8) 8
 
< 0.1%
(Missing) 60452
77.0%
ValueCountFrequency (%)
0 3450
 
4.4%
1 9587
12.2%
2 3511
 
4.5%
3 1072
 
1.4%
4 290
 
0.4%
5 88
 
0.1%
6 28
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
25 1
 
< 0.1%
22 1
 
< 0.1%
20 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 5
< 0.1%

33-no_trabajan_y_no_están_buscando
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.2%
Missing69238
Missing (%)88.2%
Infinite0
Infinite (%)0.0%
Mean0.72210822
Minimum0
Maximum20
Zeros4416
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:15.396138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94284634
Coefficient of variation (CV)1.3056856
Kurtosis34.958739
Mean0.72210822
Median Absolute Deviation (MAD)1
Skewness3.4066841
Sum6686
Variance0.88895922
MonotonicityNot monotonic
2023-10-29T11:55:15.611779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 4416
 
5.6%
1 3648
 
4.6%
2 810
 
1.0%
3 251
 
0.3%
4 83
 
0.1%
5 27
 
< 0.1%
6 11
 
< 0.1%
7 4
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
Other values (5) 5
 
< 0.1%
(Missing) 69238
88.2%
ValueCountFrequency (%)
0 4416
5.6%
1 3648
4.6%
2 810
 
1.0%
3 251
 
0.3%
4 83
 
0.1%
5 27
 
< 0.1%
6 11
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 1
 
< 0.1%
7 4
 
< 0.1%
6 11
< 0.1%
5 27
< 0.1%

33-son_jubilados_o_pensionados
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing61590
Missing (%)78.5%
Memory size613.4 KiB

33-tienen_planes_trabajar_o_similares
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.2%
Missing68890
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean0.80545436
Minimum0
Maximum30
Zeros4428
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:15.830454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0863889
Coefficient of variation (CV)1.3487902
Kurtosis74.041995
Mean0.80545436
Median Absolute Deviation (MAD)1
Skewness4.7214109
Sum7738
Variance1.1802409
MonotonicityNot monotonic
2023-10-29T11:55:16.033312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4428
 
5.6%
1 3577
 
4.6%
2 1103
 
1.4%
3 279
 
0.4%
4 124
 
0.2%
5 54
 
0.1%
6 18
 
< 0.1%
7 9
 
< 0.1%
9 4
 
< 0.1%
10 3
 
< 0.1%
Other values (6) 8
 
< 0.1%
(Missing) 68890
87.8%
ValueCountFrequency (%)
0 4428
5.6%
1 3577
4.6%
2 1103
 
1.4%
3 279
 
0.4%
4 124
 
0.2%
5 54
 
0.1%
6 18
 
< 0.1%
7 9
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
19 1
 
< 0.1%
15 1
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 4
 
< 0.1%
8 2
 
< 0.1%
7 9
< 0.1%
6 18
< 0.1%

33-trabajan
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)< 0.1%
Missing21572
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean1.9208256
Minimum0
Maximum100
Zeros421
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size613.4 KiB
2023-10-29T11:55:16.263082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum100
Range100
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2673514
Coefficient of variation (CV)0.65979511
Kurtosis740.7805
Mean1.9208256
Median Absolute Deviation (MAD)1
Skewness13.087314
Sum109343
Variance1.6061795
MonotonicityNot monotonic
2023-10-29T11:55:16.495995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 23181
29.5%
2 21363
27.2%
3 7782
 
9.9%
4 2640
 
3.4%
5 880
 
1.1%
0 421
 
0.5%
6 346
 
0.4%
7 119
 
0.2%
8 75
 
0.1%
10 44
 
0.1%
Other values (18) 74
 
0.1%
(Missing) 21572
27.5%
ValueCountFrequency (%)
0 421
 
0.5%
1 23181
29.5%
2 21363
27.2%
3 7782
 
9.9%
4 2640
 
3.4%
5 880
 
1.1%
6 346
 
0.4%
7 119
 
0.2%
8 75
 
0.1%
9 26
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
55 1
 
< 0.1%
43 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
30 2
 
< 0.1%
22 2
 
< 0.1%
21 3
< 0.1%
20 6
< 0.1%
19 1
 
< 0.1%

34-madre
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing13799
Missing (%)17.6%
Memory size613.4 KiB
Medio o Polimodal Completo
15197 
Primario Completo
15123 
Medio o Polimodal Incompleto
11029 
Terciario Completo
7720 
Primario Incompleto
6657 
Other values (3)
8972 

Length

Max length28
Median length24
Mean length22.014622
Min length17

Characters and Unicode

Total characters1424302
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimario Completo
2nd rowMedio o Polimodal Incompleto
3rd rowPrimario Incompleto
4th rowPrimario Completo
5th rowPrimario Completo

Common Values

ValueCountFrequency (%)
Medio o Polimodal Completo 15197
19.4%
Primario Completo 15123
19.3%
Medio o Polimodal Incompleto 11029
14.1%
Terciario Completo 7720
9.8%
Primario Incompleto 6657
8.5%
Universitario Completo 4273
 
5.4%
Universitario Incompleto 2462
 
3.1%
Terciario Incompleto 2237
 
2.8%
(Missing) 13799
17.6%

Length

2023-10-29T11:55:16.732517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:17.018074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
completo 42313
23.3%
medio 26226
14.4%
o 26226
14.4%
polimodal 26226
14.4%
incompleto 22385
12.3%
primario 21780
12.0%
terciario 9957
 
5.5%
universitario 6735
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o 272772
19.2%
i 136131
9.6%
117150
8.2%
l 117150
8.2%
m 112704
 
7.9%
e 107616
 
7.6%
r 76944
 
5.4%
t 71433
 
5.0%
a 64698
 
4.5%
p 64698
 
4.5%
Other values (11) 283006
19.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1151530
80.8%
Uppercase Letter 155622
 
10.9%
Space Separator 117150
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 272772
23.7%
i 136131
11.8%
l 117150
10.2%
m 112704
9.8%
e 107616
 
9.3%
r 76944
 
6.7%
t 71433
 
6.2%
a 64698
 
5.6%
p 64698
 
5.6%
d 52452
 
4.6%
Other values (4) 74932
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
P 48006
30.8%
C 42313
27.2%
M 26226
16.9%
I 22385
14.4%
T 9957
 
6.4%
U 6735
 
4.3%
Space Separator
ValueCountFrequency (%)
117150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1307152
91.8%
Common 117150
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 272772
20.9%
i 136131
10.4%
l 117150
9.0%
m 112704
8.6%
e 107616
 
8.2%
r 76944
 
5.9%
t 71433
 
5.5%
a 64698
 
4.9%
p 64698
 
4.9%
d 52452
 
4.0%
Other values (10) 230554
17.6%
Common
ValueCountFrequency (%)
117150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1424302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 272772
19.2%
i 136131
9.6%
117150
8.2%
l 117150
8.2%
m 112704
 
7.9%
e 107616
 
7.6%
r 76944
 
5.4%
t 71433
 
5.0%
a 64698
 
4.5%
p 64698
 
4.5%
Other values (11) 283006
19.9%

34-padre
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18619
Missing (%)23.7%
Memory size613.4 KiB

34-¿Tenés conocimientos de otros idiomas?
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing15059
Missing (%)19.2%
Memory size613.4 KiB
35-si
38974 
35-no
24464 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters317190
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row35-si
2nd row35-no
3rd row35-si
4th row35-no
5th row35-no

Common Values

ValueCountFrequency (%)
35-si 38974
49.7%
35-no 24464
31.2%
(Missing) 15059
 
19.2%

Length

2023-10-29T11:55:17.318115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:17.577330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
35-si 38974
61.4%
35-no 24464
38.6%

Most occurring characters

ValueCountFrequency (%)
3 63438
20.0%
5 63438
20.0%
- 63438
20.0%
s 38974
12.3%
i 38974
12.3%
n 24464
 
7.7%
o 24464
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126876
40.0%
Lowercase Letter 126876
40.0%
Dash Punctuation 63438
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 38974
30.7%
i 38974
30.7%
n 24464
19.3%
o 24464
19.3%
Decimal Number
ValueCountFrequency (%)
3 63438
50.0%
5 63438
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 63438
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190314
60.0%
Latin 126876
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 38974
30.7%
i 38974
30.7%
n 24464
19.3%
o 24464
19.3%
Common
ValueCountFrequency (%)
3 63438
33.3%
5 63438
33.3%
- 63438
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 63438
20.0%
5 63438
20.0%
- 63438
20.0%
s 38974
12.3%
i 38974
12.3%
n 24464
 
7.7%
o 24464
 
7.7%

36-especificar
Text

MISSING 

Distinct519
Distinct (%)10.8%
Missing73703
Missing (%)93.9%
Memory size613.4 KiB
2023-10-29T11:55:17.961288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length85
Median length2
Mean length5.8829787
Min length1

Characters and Unicode

Total characters28203
Distinct characters75
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique412 ?
Unique (%)8.6%

Sample

1st rowgurani
2nd rowAlemán
3rd rowitaliano
4th rowItaliano
5th rowItaliano
ValueCountFrequency (%)
si 2139
36.5%
italiano 1029
17.6%
no 447
 
7.6%
aleman 220
 
3.8%
guarani 172
 
2.9%
guaraní 153
 
2.6%
alemán 109
 
1.9%
basico 86
 
1.5%
básico 66
 
1.1%
japones 65
 
1.1%
Other values (313) 1376
23.5%
2023-10-29T11:55:18.681412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 4231
15.0%
a 3735
13.2%
n 2437
 
8.6%
o 2381
 
8.4%
S 2216
 
7.9%
l 1612
 
5.7%
t 1236
 
4.4%
e 1204
 
4.3%
1191
 
4.2%
I 859
 
3.0%
Other values (65) 7101
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21455
76.1%
Uppercase Letter 5326
 
18.9%
Space Separator 1191
 
4.2%
Other Punctuation 101
 
0.4%
Open Punctuation 44
 
0.2%
Close Punctuation 44
 
0.2%
Dash Punctuation 22
 
0.1%
Decimal Number 20
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4231
19.7%
a 3735
17.4%
n 2437
11.4%
o 2381
11.1%
l 1612
 
7.5%
t 1236
 
5.8%
e 1204
 
5.6%
r 706
 
3.3%
u 650
 
3.0%
s 604
 
2.8%
Other values (23) 2659
12.4%
Uppercase Letter
ValueCountFrequency (%)
S 2216
41.6%
I 859
 
16.1%
N 562
 
10.6%
A 479
 
9.0%
G 278
 
5.2%
L 117
 
2.2%
C 114
 
2.1%
E 104
 
2.0%
O 94
 
1.8%
T 78
 
1.5%
Other values (18) 425
 
8.0%
Other Punctuation
ValueCountFrequency (%)
, 52
51.5%
. 40
39.6%
/ 5
 
5.0%
: 2
 
2.0%
! 2
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 7
35.0%
2 5
25.0%
0 4
20.0%
3 3
15.0%
5 1
 
5.0%
Space Separator
ValueCountFrequency (%)
1191
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26781
95.0%
Common 1422
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4231
15.8%
a 3735
13.9%
n 2437
 
9.1%
o 2381
 
8.9%
S 2216
 
8.3%
l 1612
 
6.0%
t 1236
 
4.6%
e 1204
 
4.5%
I 859
 
3.2%
r 706
 
2.6%
Other values (51) 6164
23.0%
Common
ValueCountFrequency (%)
1191
83.8%
, 52
 
3.7%
( 44
 
3.1%
) 44
 
3.1%
. 40
 
2.8%
- 22
 
1.5%
1 7
 
0.5%
/ 5
 
0.4%
2 5
 
0.4%
0 4
 
0.3%
Other values (4) 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27680
98.1%
None 523
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4231
15.3%
a 3735
13.5%
n 2437
 
8.8%
o 2381
 
8.6%
S 2216
 
8.0%
l 1612
 
5.8%
t 1236
 
4.5%
e 1204
 
4.3%
1191
 
4.3%
I 859
 
3.1%
Other values (55) 6578
23.8%
None
ValueCountFrequency (%)
á 188
35.9%
í 170
32.5%
é 109
20.8%
ñ 39
 
7.5%
ó 5
 
1.0%
Ñ 5
 
1.0%
Á 4
 
0.8%
ú 1
 
0.2%
Í 1
 
0.2%
ü 1
 
0.2%

36-francés
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing76036
Missing (%)96.9%
Memory size613.4 KiB
Bueno
1621 
Intermedio
775 
Avanzado
 
65

Length

Max length10
Median length5
Mean length6.6537993
Min length5

Characters and Unicode

Total characters16375
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBueno
2nd rowBueno
3rd rowBueno
4th rowIntermedio
5th rowIntermedio

Common Values

ValueCountFrequency (%)
Bueno 1621
 
2.1%
Intermedio 775
 
1.0%
Avanzado 65
 
0.1%
(Missing) 76036
96.9%

Length

2023-10-29T11:55:18.869527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:18.951350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bueno 1621
65.9%
intermedio 775
31.5%
avanzado 65
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 3171
19.4%
n 2461
15.0%
o 2461
15.0%
B 1621
9.9%
u 1621
9.9%
d 840
 
5.1%
I 775
 
4.7%
t 775
 
4.7%
r 775
 
4.7%
m 775
 
4.7%
Other values (5) 1100
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13914
85.0%
Uppercase Letter 2461
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3171
22.8%
n 2461
17.7%
o 2461
17.7%
u 1621
11.7%
d 840
 
6.0%
t 775
 
5.6%
r 775
 
5.6%
m 775
 
5.6%
i 775
 
5.6%
a 130
 
0.9%
Other values (2) 130
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 1621
65.9%
I 775
31.5%
A 65
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 16375
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3171
19.4%
n 2461
15.0%
o 2461
15.0%
B 1621
9.9%
u 1621
9.9%
d 840
 
5.1%
I 775
 
4.7%
t 775
 
4.7%
r 775
 
4.7%
m 775
 
4.7%
Other values (5) 1100
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3171
19.4%
n 2461
15.0%
o 2461
15.0%
B 1621
9.9%
u 1621
9.9%
d 840
 
5.1%
I 775
 
4.7%
t 775
 
4.7%
r 775
 
4.7%
m 775
 
4.7%
Other values (5) 1100
 
6.7%

36-inglés
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing41204
Missing (%)52.5%
Memory size613.4 KiB
Bueno
17601 
Intermedio
16133 
Avanzado
3559 

Length

Max length10
Median length8
Mean length7.4493068
Min length5

Characters and Unicode

Total characters277807
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermedio
2nd rowBueno
3rd rowIntermedio
4th rowBueno
5th rowIntermedio

Common Values

ValueCountFrequency (%)
Bueno 17601
22.4%
Intermedio 16133
 
20.6%
Avanzado 3559
 
4.5%
(Missing) 41204
52.5%

Length

2023-10-29T11:55:19.023166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:19.106998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bueno 17601
47.2%
intermedio 16133
43.3%
avanzado 3559
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 49867
18.0%
n 37293
13.4%
o 37293
13.4%
d 19692
 
7.1%
B 17601
 
6.3%
u 17601
 
6.3%
I 16133
 
5.8%
t 16133
 
5.8%
r 16133
 
5.8%
m 16133
 
5.8%
Other values (5) 33928
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 240514
86.6%
Uppercase Letter 37293
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49867
20.7%
n 37293
15.5%
o 37293
15.5%
d 19692
 
8.2%
u 17601
 
7.3%
t 16133
 
6.7%
r 16133
 
6.7%
m 16133
 
6.7%
i 16133
 
6.7%
a 7118
 
3.0%
Other values (2) 7118
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
B 17601
47.2%
I 16133
43.3%
A 3559
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 277807
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49867
18.0%
n 37293
13.4%
o 37293
13.4%
d 19692
 
7.1%
B 17601
 
6.3%
u 17601
 
6.3%
I 16133
 
5.8%
t 16133
 
5.8%
r 16133
 
5.8%
m 16133
 
5.8%
Other values (5) 33928
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49867
18.0%
n 37293
13.4%
o 37293
13.4%
d 19692
 
7.1%
B 17601
 
6.3%
u 17601
 
6.3%
I 16133
 
5.8%
t 16133
 
5.8%
r 16133
 
5.8%
m 16133
 
5.8%
Other values (5) 33928
12.2%

36-otros
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct32
Distinct (%)1.7%
Missing76588
Missing (%)97.6%
Memory size613.4 KiB
Bueno
1040 
Intermedio
609 
Avanzado
183 
Italiano
 
22
Guaraní
 
10
Other values (27)
 
45

Length

Max length25
Median length5
Mean length7.0178104
Min length4

Characters and Unicode

Total characters13397
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)1.1%

Sample

1st rowBueno
2nd rowBueno
3rd rowIntermedio
4th rowBueno
5th rowBueno

Common Values

ValueCountFrequency (%)
Bueno 1040
 
1.3%
Intermedio 609
 
0.8%
Avanzado 183
 
0.2%
Italiano 22
 
< 0.1%
Guaraní 10
 
< 0.1%
Guarani 7
 
< 0.1%
Aleman 5
 
< 0.1%
Alemán 4
 
< 0.1%
italiano 4
 
< 0.1%
Ruso 2
 
< 0.1%
Other values (22) 23
 
< 0.1%
(Missing) 76588
97.6%

Length

2023-10-29T11:55:19.185105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bueno 1040
54.1%
intermedio 611
31.8%
avanzado 183
 
9.5%
italiano 32
 
1.7%
guaraní 11
 
0.6%
guarani 8
 
0.4%
alemán 7
 
0.4%
aleman 6
 
0.3%
ruso 3
 
0.2%
coreano 3
 
0.2%
Other values (16) 18
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 2284
17.0%
n 1911
14.3%
o 1884
14.1%
u 1065
7.9%
B 1042
7.8%
d 797
 
5.9%
i 664
 
5.0%
t 643
 
4.8%
I 638
 
4.8%
r 636
 
4.7%
Other values (36) 1833
13.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11465
85.6%
Uppercase Letter 1911
 
14.3%
Space Separator 16
 
0.1%
Other Punctuation 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2284
19.9%
n 1911
16.7%
o 1884
16.4%
u 1065
9.3%
d 797
 
7.0%
i 664
 
5.8%
t 643
 
5.6%
r 636
 
5.5%
m 624
 
5.4%
a 490
 
4.3%
Other values (15) 467
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
B 1042
54.5%
I 638
33.4%
A 197
 
10.3%
G 18
 
0.9%
L 2
 
0.1%
N 2
 
0.1%
R 2
 
0.1%
C 2
 
0.1%
J 1
 
0.1%
H 1
 
0.1%
Other values (6) 6
 
0.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13376
99.8%
Common 21
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2284
17.1%
n 1911
14.3%
o 1884
14.1%
u 1065
8.0%
B 1042
7.8%
d 797
 
6.0%
i 664
 
5.0%
t 643
 
4.8%
I 638
 
4.8%
r 636
 
4.8%
Other values (31) 1812
13.5%
Common
ValueCountFrequency (%)
16
76.2%
, 2
 
9.5%
- 1
 
4.8%
( 1
 
4.8%
) 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13374
99.8%
None 23
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2284
17.1%
n 1911
14.3%
o 1884
14.1%
u 1065
8.0%
B 1042
7.8%
d 797
 
6.0%
i 664
 
5.0%
t 643
 
4.8%
I 638
 
4.8%
r 636
 
4.8%
Other values (31) 1810
13.5%
None
ValueCountFrequency (%)
í 11
47.8%
á 7
30.4%
é 3
 
13.0%
ñ 1
 
4.3%
Á 1
 
4.3%

36-portugués
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing72116
Missing (%)91.9%
Memory size613.4 KiB
Bueno
3812 
Intermedio
2275 
Avanzado
 
294

Length

Max length10
Median length5
Mean length6.9208588
Min length5

Characters and Unicode

Total characters44162
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntermedio
2nd rowBueno
3rd rowIntermedio
4th rowBueno
5th rowBueno

Common Values

ValueCountFrequency (%)
Bueno 3812
 
4.9%
Intermedio 2275
 
2.9%
Avanzado 294
 
0.4%
(Missing) 72116
91.9%

Length

2023-10-29T11:55:19.261815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:19.339637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bueno 3812
59.7%
intermedio 2275
35.7%
avanzado 294
 
4.6%

Most occurring characters

ValueCountFrequency (%)
e 8362
18.9%
n 6381
14.4%
o 6381
14.4%
B 3812
8.6%
u 3812
8.6%
d 2569
 
5.8%
I 2275
 
5.2%
t 2275
 
5.2%
r 2275
 
5.2%
m 2275
 
5.2%
Other values (5) 3745
8.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37781
85.6%
Uppercase Letter 6381
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8362
22.1%
n 6381
16.9%
o 6381
16.9%
u 3812
10.1%
d 2569
 
6.8%
t 2275
 
6.0%
r 2275
 
6.0%
m 2275
 
6.0%
i 2275
 
6.0%
a 588
 
1.6%
Other values (2) 588
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 3812
59.7%
I 2275
35.7%
A 294
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 44162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8362
18.9%
n 6381
14.4%
o 6381
14.4%
B 3812
8.6%
u 3812
8.6%
d 2569
 
5.8%
I 2275
 
5.2%
t 2275
 
5.2%
r 2275
 
5.2%
m 2275
 
5.2%
Other values (5) 3745
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8362
18.9%
n 6381
14.4%
o 6381
14.4%
B 3812
8.6%
u 3812
8.6%
d 2569
 
5.8%
I 2275
 
5.2%
t 2275
 
5.2%
r 2275
 
5.2%
m 2275
 
5.2%
Other values (5) 3745
8.5%

37-especificar
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35549
Missing (%)45.3%
Memory size613.4 KiB

37-otros
Text

MISSING 

Distinct54
Distinct (%)0.3%
Missing62668
Missing (%)79.8%
Memory size613.4 KiB
2023-10-29T11:55:19.434530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length74
Median length2
Mean length2.7037084
Min length1

Characters and Unicode

Total characters42797
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)0.3%

Sample

1st rowSi
2nd rowSi
3rd rowNo
4th rowSi
5th rowNo
ValueCountFrequency (%)
no 7808
60.5%
si 2801
 
21.7%
bueno 1046
 
8.1%
intermedio 895
 
6.9%
avanzado 261
 
2.0%
colegio 9
 
0.1%
escuela 8
 
0.1%
ciber 7
 
0.1%
tengo 6
 
< 0.1%
en 6
 
< 0.1%
Other values (41) 61
 
0.5%
2023-10-29T11:55:19.659303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 10056
23.5%
N 7808
18.2%
4730
11.1%
i 3734
 
8.7%
e 2909
 
6.8%
S 2802
 
6.5%
n 2230
 
5.2%
d 1171
 
2.7%
u 1067
 
2.5%
B 1046
 
2.4%
Other values (33) 5244
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25160
58.8%
Uppercase Letter 12850
30.0%
Control 4730
 
11.1%
Space Separator 55
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 10056
40.0%
i 3734
 
14.8%
e 2909
 
11.6%
n 2230
 
8.9%
d 1171
 
4.7%
u 1067
 
4.2%
r 930
 
3.7%
t 910
 
3.6%
m 907
 
3.6%
a 574
 
2.3%
Other values (16) 672
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
N 7808
60.8%
S 2802
 
21.8%
B 1046
 
8.1%
I 895
 
7.0%
A 261
 
2.0%
C 18
 
0.1%
E 9
 
0.1%
P 3
 
< 0.1%
T 2
 
< 0.1%
F 2
 
< 0.1%
Other values (3) 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
: 1
50.0%
Control
ValueCountFrequency (%)
4730
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38010
88.8%
Common 4787
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 10056
26.5%
N 7808
20.5%
i 3734
 
9.8%
e 2909
 
7.7%
S 2802
 
7.4%
n 2230
 
5.9%
d 1171
 
3.1%
u 1067
 
2.8%
B 1046
 
2.8%
r 930
 
2.4%
Other values (29) 4257
11.2%
Common
ValueCountFrequency (%)
4730
98.8%
55
 
1.1%
, 1
 
< 0.1%
: 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42794
> 99.9%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 10056
23.5%
N 7808
18.2%
4730
11.1%
i 3734
 
8.7%
e 2909
 
6.8%
S 2802
 
6.5%
n 2230
 
5.2%
d 1171
 
2.7%
u 1067
 
2.5%
B 1046
 
2.4%
Other values (30) 5241
12.2%
None
ValueCountFrequency (%)
ó 1
33.3%
á 1
33.3%
ñ 1
33.3%

37-tu_casa
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing17121
Missing (%)21.8%
Memory size613.4 KiB
Si
53987 
No
7389 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters122752
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
Si 53987
68.8%
No 7389
 
9.4%
(Missing) 17121
 
21.8%

Length

2023-10-29T11:55:19.751183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:19.835818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
si 53987
88.0%
no 7389
 
12.0%

Most occurring characters

ValueCountFrequency (%)
S 53987
44.0%
i 53987
44.0%
N 7389
 
6.0%
o 7389
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 61376
50.0%
Lowercase Letter 61376
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 53987
88.0%
N 7389
 
12.0%
Lowercase Letter
ValueCountFrequency (%)
i 53987
88.0%
o 7389
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 122752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 53987
44.0%
i 53987
44.0%
N 7389
 
6.0%
o 7389
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 53987
44.0%
i 53987
44.0%
N 7389
 
6.0%
o 7389
 
6.0%

37-tu_trabajo
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing51740
Missing (%)65.9%
Memory size613.4 KiB
No
14367 
Si
12390 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters53514
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSi
2nd rowSi
3rd rowSi
4th rowSi
5th rowSi

Common Values

ValueCountFrequency (%)
No 14367
 
18.3%
Si 12390
 
15.8%
(Missing) 51740
65.9%

Length

2023-10-29T11:55:19.903359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:19.987567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 14367
53.7%
si 12390
46.3%

Most occurring characters

ValueCountFrequency (%)
N 14367
26.8%
o 14367
26.8%
S 12390
23.2%
i 12390
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26757
50.0%
Lowercase Letter 26757
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 14367
53.7%
S 12390
46.3%
Lowercase Letter
ValueCountFrequency (%)
o 14367
53.7%
i 12390
46.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 53514
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 14367
26.8%
o 14367
26.8%
S 12390
23.2%
i 12390
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 14367
26.8%
o 14367
26.8%
S 12390
23.2%
i 12390
23.2%

38-especificar
Text

MISSING 

Distinct496
Distinct (%)69.9%
Missing77787
Missing (%)99.1%
Memory size613.4 KiB
2023-10-29T11:55:20.125248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length59
Median length45
Mean length16.292958
Min length1

Characters and Unicode

Total characters11568
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique423 ?
Unique (%)59.6%

Sample

1st rowDiseñador Web
2nd rowDiseño Web y Programación
3rd rowAplicaciones graficas
4th rowEditor de audio y audiovisual
5th rowbasico
ValueCountFrequency (%)
de 187
 
10.4%
y 73
 
4.0%
power 66
 
3.7%
point 65
 
3.6%
diseño 60
 
3.3%
office 60
 
3.3%
pc 47
 
2.6%
autocad 40
 
2.2%
programas 33
 
1.8%
tango 33
 
1.8%
Other values (369) 1139
63.2%
2023-10-29T11:55:20.415023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1171
 
10.1%
o 1102
 
9.5%
e 1027
 
8.9%
a 843
 
7.3%
i 747
 
6.5%
r 686
 
5.9%
s 580
 
5.0%
d 500
 
4.3%
c 487
 
4.2%
n 474
 
4.1%
Other values (64) 3951
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8829
76.3%
Uppercase Letter 1497
 
12.9%
Space Separator 1171
 
10.1%
Decimal Number 28
 
0.2%
Other Punctuation 20
 
0.2%
Close Punctuation 11
 
0.1%
Open Punctuation 11
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1102
12.5%
e 1027
11.6%
a 843
9.5%
i 747
 
8.5%
r 686
 
7.8%
s 580
 
6.6%
d 500
 
5.7%
c 487
 
5.5%
n 474
 
5.4%
t 449
 
5.1%
Other values (22) 1934
21.9%
Uppercase Letter
ValueCountFrequency (%)
P 215
14.4%
A 161
10.8%
O 137
9.2%
E 111
 
7.4%
C 102
 
6.8%
D 97
 
6.5%
S 95
 
6.3%
R 93
 
6.2%
T 78
 
5.2%
I 75
 
5.0%
Other values (18) 333
22.2%
Decimal Number
ValueCountFrequency (%)
2 7
25.0%
1 5
17.9%
0 5
17.9%
7 4
14.3%
8 2
 
7.1%
3 2
 
7.1%
9 1
 
3.6%
4 1
 
3.6%
5 1
 
3.6%
Space Separator
ValueCountFrequency (%)
1171
100.0%
Other Punctuation
ValueCountFrequency (%)
. 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10326
89.3%
Common 1242
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1102
 
10.7%
e 1027
 
9.9%
a 843
 
8.2%
i 747
 
7.2%
r 686
 
6.6%
s 580
 
5.6%
d 500
 
4.8%
c 487
 
4.7%
n 474
 
4.6%
t 449
 
4.3%
Other values (50) 3431
33.2%
Common
ValueCountFrequency (%)
1171
94.3%
. 20
 
1.6%
) 11
 
0.9%
( 11
 
0.9%
2 7
 
0.6%
1 5
 
0.4%
0 5
 
0.4%
7 4
 
0.3%
8 2
 
0.2%
3 2
 
0.2%
Other values (4) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11432
98.8%
None 136
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1171
 
10.2%
o 1102
 
9.6%
e 1027
 
9.0%
a 843
 
7.4%
i 747
 
6.5%
r 686
 
6.0%
s 580
 
5.1%
d 500
 
4.4%
c 487
 
4.3%
n 474
 
4.1%
Other values (56) 3815
33.4%
None
ValueCountFrequency (%)
ñ 64
47.1%
ó 38
27.9%
á 20
 
14.7%
Ñ 5
 
3.7%
í 3
 
2.2%
é 3
 
2.2%
Ó 2
 
1.5%
ú 1
 
0.7%

38-navegación_por_internet
Text

MISSING 

Distinct51
Distinct (%)0.2%
Missing52424
Missing (%)66.8%
Memory size613.4 KiB
2023-10-29T11:55:20.562891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length38
Median length37
Mean length7.5985119
Min length3

Characters and Unicode

Total characters198116
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.2%

Sample

1st rowIntermedio
2nd rowAvanzado
3rd rowBueno
4th rowBueno
5th rowIntermedio
ValueCountFrequency (%)
bueno 9419
36.0%
intermedio 8639
33.0%
avanzado 7965
30.4%
de 12
 
< 0.1%
point 5
 
< 0.1%
y 5
 
< 0.1%
power 4
 
< 0.1%
photoshop 4
 
< 0.1%
sistemas 4
 
< 0.1%
diseño 4
 
< 0.1%
Other values (75) 97
 
0.4%
2023-10-29T11:55:20.851430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 26780
13.5%
o 26114
13.2%
n 26056
13.2%
d 16634
 
8.4%
a 15983
 
8.1%
u 9428
 
4.8%
B 9421
 
4.8%
i 8702
 
4.4%
r 8699
 
4.4%
t 8678
 
4.4%
Other values (44) 41621
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 171888
86.8%
Uppercase Letter 26129
 
13.2%
Space Separator 86
 
< 0.1%
Other Punctuation 9
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26780
15.6%
o 26114
15.2%
n 26056
15.2%
d 16634
9.7%
a 15983
9.3%
u 9428
 
5.5%
i 8702
 
5.1%
r 8699
 
5.1%
t 8678
 
5.0%
m 8662
 
5.0%
Other values (20) 16152
9.4%
Uppercase Letter
ValueCountFrequency (%)
B 9421
36.1%
I 8642
33.1%
A 7977
30.5%
P 22
 
0.1%
S 11
 
< 0.1%
O 11
 
< 0.1%
D 9
 
< 0.1%
C 8
 
< 0.1%
R 5
 
< 0.1%
E 5
 
< 0.1%
Other values (9) 18
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
. 1
 
11.1%
Space Separator
ValueCountFrequency (%)
86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 198017
> 99.9%
Common 99
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26780
13.5%
o 26114
13.2%
n 26056
13.2%
d 16634
8.4%
a 15983
 
8.1%
u 9428
 
4.8%
B 9421
 
4.8%
i 8702
 
4.4%
r 8699
 
4.4%
t 8678
 
4.4%
Other values (39) 41522
21.0%
Common
ValueCountFrequency (%)
86
86.9%
, 8
 
8.1%
( 2
 
2.0%
) 2
 
2.0%
. 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198100
> 99.9%
None 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26780
13.5%
o 26114
13.2%
n 26056
13.2%
d 16634
 
8.4%
a 15983
 
8.1%
u 9428
 
4.8%
B 9421
 
4.8%
i 8702
 
4.4%
r 8699
 
4.4%
t 8678
 
4.4%
Other values (40) 41605
21.0%
None
ValueCountFrequency (%)
ó 7
43.8%
ñ 5
31.2%
á 3
18.8%
í 1
 
6.2%

38-otros
Text

MISSING 

Distinct788
Distinct (%)23.9%
Missing75196
Missing (%)95.8%
Memory size613.4 KiB
2023-10-29T11:55:21.046008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length99
Mean length11.734626
Min length1

Characters and Unicode

Total characters38736
Distinct characters82
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique712 ?
Unique (%)21.6%

Sample

1st rowIntermedio
2nd rowAvanzado
3rd rowAvanzado
4th rowIntermedio
5th rowIntermedio
ValueCountFrequency (%)
bueno 998
17.9%
intermedio 769
 
13.8%
avanzado 521
 
9.3%
de 309
 
5.5%
power 115
 
2.1%
point 114
 
2.0%
y 114
 
2.0%
word 93
 
1.7%
diseño 80
 
1.4%
office 73
 
1.3%
Other values (680) 2392
42.9%
2023-10-29T11:55:21.334335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4495
 
11.6%
o 4363
 
11.3%
n 3241
 
8.4%
a 2396
 
6.2%
2324
 
6.0%
d 2244
 
5.8%
i 2211
 
5.7%
r 1996
 
5.2%
t 1661
 
4.3%
u 1346
 
3.5%
Other values (72) 12459
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31208
80.6%
Uppercase Letter 4624
 
11.9%
Space Separator 2324
 
6.0%
Other Punctuation 452
 
1.2%
Decimal Number 45
 
0.1%
Dash Punctuation 32
 
0.1%
Open Punctuation 25
 
0.1%
Close Punctuation 24
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4495
14.4%
o 4363
14.0%
n 3241
10.4%
a 2396
 
7.7%
d 2244
 
7.2%
i 2211
 
7.1%
r 1996
 
6.4%
t 1661
 
5.3%
u 1346
 
4.3%
m 1210
 
3.9%
Other values (22) 6045
19.4%
Uppercase Letter
ValueCountFrequency (%)
B 1030
22.3%
I 869
18.8%
A 715
15.5%
P 422
9.1%
E 209
 
4.5%
O 188
 
4.1%
S 173
 
3.7%
C 139
 
3.0%
D 135
 
2.9%
R 131
 
2.8%
Other values (19) 613
13.3%
Other Punctuation
ValueCountFrequency (%)
, 343
75.9%
. 85
 
18.8%
/ 16
 
3.5%
: 4
 
0.9%
# 1
 
0.2%
¿ 1
 
0.2%
? 1
 
0.2%
@ 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
3 17
37.8%
2 7
15.6%
0 7
15.6%
1 6
 
13.3%
4 3
 
6.7%
6 2
 
4.4%
5 2
 
4.4%
8 1
 
2.2%
Space Separator
ValueCountFrequency (%)
2324
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35832
92.5%
Common 2904
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4495
12.5%
o 4363
12.2%
n 3241
 
9.0%
a 2396
 
6.7%
d 2244
 
6.3%
i 2211
 
6.2%
r 1996
 
5.6%
t 1661
 
4.6%
u 1346
 
3.8%
m 1210
 
3.4%
Other values (51) 10669
29.8%
Common
ValueCountFrequency (%)
2324
80.0%
, 343
 
11.8%
. 85
 
2.9%
- 32
 
1.1%
( 25
 
0.9%
) 24
 
0.8%
3 17
 
0.6%
/ 16
 
0.6%
2 7
 
0.2%
0 7
 
0.2%
Other values (11) 24
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38408
99.2%
None 328
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4495
 
11.7%
o 4363
 
11.4%
n 3241
 
8.4%
a 2396
 
6.2%
2324
 
6.1%
d 2244
 
5.8%
i 2211
 
5.8%
r 1996
 
5.2%
t 1661
 
4.3%
u 1346
 
3.5%
Other values (62) 12131
31.6%
None
ValueCountFrequency (%)
ó 151
46.0%
ñ 81
24.7%
á 65
19.8%
é 10
 
3.0%
í 9
 
2.7%
ú 5
 
1.5%
Ñ 4
 
1.2%
¿ 1
 
0.3%
Á 1
 
0.3%
Ó 1
 
0.3%

38-planilla_de_cálculo
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing21771
Missing (%)27.7%
Memory size613.4 KiB
Bueno
23418 
Intermedio
19349 
Avanzado
13959 

Length

Max length10
Median length8
Mean length7.4437119
Min length5

Characters and Unicode

Total characters422252
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBueno
2nd rowBueno
3rd rowBueno
4th rowIntermedio
5th rowIntermedio

Common Values

ValueCountFrequency (%)
Bueno 23418
29.8%
Intermedio 19349
24.6%
Avanzado 13959
17.8%
(Missing) 21771
27.7%

Length

2023-10-29T11:55:21.434123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:21.515930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bueno 23418
41.3%
intermedio 19349
34.1%
avanzado 13959
24.6%

Most occurring characters

ValueCountFrequency (%)
e 62116
14.7%
n 56726
13.4%
o 56726
13.4%
d 33308
7.9%
a 27918
 
6.6%
B 23418
 
5.5%
u 23418
 
5.5%
I 19349
 
4.6%
t 19349
 
4.6%
r 19349
 
4.6%
Other values (5) 80575
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365526
86.6%
Uppercase Letter 56726
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 62116
17.0%
n 56726
15.5%
o 56726
15.5%
d 33308
9.1%
a 27918
7.6%
u 23418
 
6.4%
t 19349
 
5.3%
r 19349
 
5.3%
m 19349
 
5.3%
i 19349
 
5.3%
Other values (2) 27918
7.6%
Uppercase Letter
ValueCountFrequency (%)
B 23418
41.3%
I 19349
34.1%
A 13959
24.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 422252
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 62116
14.7%
n 56726
13.4%
o 56726
13.4%
d 33308
7.9%
a 27918
 
6.6%
B 23418
 
5.5%
u 23418
 
5.5%
I 19349
 
4.6%
t 19349
 
4.6%
r 19349
 
4.6%
Other values (5) 80575
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 422252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 62116
14.7%
n 56726
13.4%
o 56726
13.4%
d 33308
7.9%
a 27918
 
6.6%
B 23418
 
5.5%
u 23418
 
5.5%
I 19349
 
4.6%
t 19349
 
4.6%
r 19349
 
4.6%
Other values (5) 80575
19.1%

38-procesador_de_texto
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing23982
Missing (%)30.6%
Memory size613.4 KiB
Bueno
25631 
Intermedio
22480 
Avanzado
6404 

Length

Max length10
Median length8
Mean length7.4142346
Min length5

Characters and Unicode

Total characters404187
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBueno
2nd rowBueno
3rd rowBueno
4th rowIntermedio
5th rowBueno

Common Values

ValueCountFrequency (%)
Bueno 25631
32.7%
Intermedio 22480
28.6%
Avanzado 6404
 
8.2%
(Missing) 23982
30.6%

Length

2023-10-29T11:55:21.593035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-29T11:55:21.675462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bueno 25631
47.0%
intermedio 22480
41.2%
avanzado 6404
 
11.7%

Most occurring characters

ValueCountFrequency (%)
e 70591
17.5%
n 54515
13.5%
o 54515
13.5%
d 28884
7.1%
B 25631
 
6.3%
u 25631
 
6.3%
I 22480
 
5.6%
t 22480
 
5.6%
r 22480
 
5.6%
m 22480
 
5.6%
Other values (5) 54500
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 349672
86.5%
Uppercase Letter 54515
 
13.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 70591
20.2%
n 54515
15.6%
o 54515
15.6%
d 28884
8.3%
u 25631
 
7.3%
t 22480
 
6.4%
r 22480
 
6.4%
m 22480
 
6.4%
i 22480
 
6.4%
a 12808
 
3.7%
Other values (2) 12808
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 25631
47.0%
I 22480
41.2%
A 6404
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 404187
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 70591
17.5%
n 54515
13.5%
o 54515
13.5%
d 28884
7.1%
B 25631
 
6.3%
u 25631
 
6.3%
I 22480
 
5.6%
t 22480
 
5.6%
r 22480
 
5.6%
m 22480
 
5.6%
Other values (5) 54500
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 404187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 70591
17.5%
n 54515
13.5%
o 54515
13.5%
d 28884
7.1%
B 25631
 
6.3%
u 25631
 
6.3%
I 22480
 
5.6%
t 22480
 
5.6%
r 22480
 
5.6%
m 22480
 
5.6%
Other values (5) 54500
13.5%

Interactions

2023-10-29T11:54:28.232845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:39.252558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:45.543247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:53.243534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:01.577006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:02.651031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:04.888593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:07.393779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:09.551695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:12.038522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:14.332959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:20.953652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:24.691920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:32.478549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:14.556099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:21.436471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:24.922889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:27.809324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:32.701571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:44.797559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:52.398793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:00.261843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:06.735282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:14.651343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:21.042987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:27.077169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:33.034587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:38.961881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:45.059965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:09.857659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:12.346455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:21.690023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:25.484826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:27.914206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:32.989078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:45.043820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:52:52.708009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:00.692269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:07.087812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:14.924293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:21.271100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:27.326927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:33.290920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:39.241289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:45.411778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:51.564451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:53:57.978218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:00.932532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:03.018321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:05.320161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:22.023523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:25.591973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:28.023429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:53:51.874819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:12.564953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-29T11:54:25.706795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-29T11:54:28.133825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-29T11:55:21.796170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
carreracohortesededocumento3-minutos5-es_gratuita5-me_la_recomendaron5-no_se_toma_examen_de_ingreso5-otras_razones5-por_el_prestigio_de_la_carrera5-por_la_cercanía5-por_su_calidad_académica5-se_dicta_la_carrera_que_prefiero31-especificar32-medio_o_polimodal32-primario_o_egb32-terciario_no_universitario32-universitarios33-no_trabajan_pero_estan_buscando_trabajo33-no_trabajan_porque_estudian33-no_trabajan_y_no_están_buscando33-tienen_planes_trabajar_o_similares33-trabajan4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar?7-¿Qué nivel estás cursando?8-¿Cuál fue el nivel mas alto que terminaste?9-¿Terminaste o estás cursando tus estudios en un Colegio?10-¿El Colegio es… (Privado/Público)11-Número del Colegio17-¿Tenés Obra Social y/o Mutual?18-¿Trabajás actualmente?19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es:20-En tu trabajo principal sos...21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal?23-¿Tu trabajo principal es?24-En una semana normal de trabajo ¿cuántas horas trabajás?25-Tus horarios son…26-¿Qué momentos del día abarca tu jornada de trabajo?27-¿Podés cambiar los horarios de trabajo?28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan)34-madre34-¿Tenés conocimientos de otros idiomas?36-francés36-inglés36-otros36-portugués37-tu_casa37-tu_trabajo38-planilla_de_cálculo38-procesador_de_texto
carrera1.0000.0730.1820.0710.0230.007-0.0070.0260.017-0.0120.031-0.023-0.0280.0230.0030.006-0.003-0.0280.0090.009-0.0040.0310.0000.1110.1130.1050.1480.0760.0800.1020.0910.0720.0700.0330.0660.0490.0360.0330.0520.0500.0610.1150.0470.0620.0000.0220.1060.1330.0600.034
cohorte0.0731.000-0.0640.489-0.005-0.041-0.000-0.0270.0060.033-0.0140.0040.009-0.0610.001-0.0460.0370.006-0.008-0.0220.0040.098-0.0400.0270.0560.0650.0590.3010.0450.0820.0650.0280.0730.0400.0720.0730.0420.0590.0380.0420.0270.0450.0310.0280.0380.0170.1750.1180.1660.082
sede0.182-0.0641.000-0.032-0.0910.027-0.0620.0680.059-0.0180.089-0.011-0.0490.0090.0230.0170.0170.0050.028-0.0030.0060.0310.0380.1100.0780.0680.0290.0680.0350.0700.0220.0310.0500.0180.0420.0430.0340.0230.0470.0250.0290.0210.0550.0190.0000.0340.0220.0820.0120.023
documento0.0710.489-0.0321.0000.0540.0420.018-0.0380.0410.0510.0090.0420.0120.1350.0570.068-0.008-0.045-0.088-0.100-0.0110.0620.1051.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
3-minutos0.023-0.005-0.0910.0541.000-0.041-0.072-0.090-0.010-0.0400.152-0.0430.0280.073-0.0100.031-0.044-0.0960.0360.008-0.0140.0380.0050.0000.0000.0170.0100.0000.0000.0000.0000.0030.0000.0070.0000.0070.0000.0000.0000.0060.0110.0001.0000.0001.0000.0000.0000.0110.0000.006
5-es_gratuita0.007-0.0410.0270.042-0.0411.0000.0590.4410.289-0.0650.178-0.117-0.1660.0050.0220.010-0.0140.010-0.104-0.034-0.048-0.1090.0400.0080.0230.0180.0080.0290.0060.0380.0250.0100.0000.0100.0040.0060.0000.0000.0050.0130.0110.0190.0000.0090.0000.0000.0210.0000.0070.009
5-me_la_recomendaron-0.007-0.000-0.0620.018-0.0720.0591.0000.1730.3710.2710.1860.3150.099-0.006-0.0160.013-0.067-0.094-0.044-0.053-0.039-0.072-0.0190.0000.0000.0230.0001.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0031.0001.0001.0001.0001.0001.0001.0001.000
5-no_se_toma_examen_de_ingreso0.026-0.0270.068-0.038-0.0900.4410.1731.0000.2660.0470.2640.0290.059-0.0220.0110.018-0.020-0.008-0.061-0.029-0.052-0.0930.0160.0000.0000.0280.0471.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5-otras_razones0.0170.0060.0590.041-0.0100.2890.3710.2661.0000.3920.3240.3370.2240.013-0.0040.022-0.070-0.091-0.030-0.014-0.024-0.0450.0120.0000.0000.0280.0000.0000.0000.0000.0000.0050.0000.0220.0000.0310.0190.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.019
5-por_el_prestigio_de_la_carrera-0.0120.033-0.0180.051-0.040-0.0650.2710.0470.3921.0000.0350.6130.184-0.001-0.0030.018-0.063-0.089-0.015-0.054-0.011-0.017-0.0060.0530.0180.0180.0180.0090.0230.0080.0190.0210.0240.0100.0390.0170.0190.0190.0000.0000.0100.0240.0000.0000.1390.0000.0400.0550.0080.016
5-por_la_cercanía0.031-0.0140.0890.0090.1520.1780.1860.2640.3240.0351.0000.0130.0540.025-0.0010.018-0.057-0.059-0.021-0.036-0.047-0.0320.0070.0000.0021.0000.0000.0000.0000.0000.0000.0010.0000.0000.0040.0000.0030.0130.0140.0000.0010.0051.0000.0001.0001.0000.0000.0000.0000.000
5-por_su_calidad_académica-0.0230.004-0.0110.042-0.043-0.1170.3150.0290.3370.6130.0131.0000.0500.013-0.0030.029-0.061-0.091-0.043-0.060-0.013-0.0170.0020.0000.0000.0000.0121.0000.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0001.0000.0001.0001.0000.0001.0000.0000.000
5-se_dicta_la_carrera_que_prefiero-0.0280.009-0.0490.0120.028-0.1660.0990.0590.2240.1840.0540.0501.000-0.010-0.0100.006-0.042-0.033-0.002-0.0460.012-0.013-0.0030.0011.0000.0180.0101.0001.0000.0001.0000.0001.0001.0001.0001.0001.0001.0001.0000.0040.0001.0001.0001.0001.0001.0000.0031.0001.0000.011
31-especificar0.023-0.0610.0090.1350.0730.005-0.006-0.0220.013-0.0010.0250.013-0.0101.0000.2520.340-0.029-0.0450.2030.3550.2100.1780.3090.0070.0070.0220.0190.0950.0000.0940.1150.0260.0340.0170.0630.0650.0170.0220.0040.0060.0370.0610.0400.0320.0000.0210.0630.0820.0280.018
32-medio_o_polimodal0.0030.0010.0230.057-0.0100.022-0.0160.011-0.004-0.003-0.001-0.003-0.0100.2521.0000.2500.2570.2460.2150.1010.0940.1490.3800.0010.0000.0000.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0000.0190.0001.0000.0001.0000.0000.0001.0000.0050.000
32-primario_o_egb0.006-0.0460.0170.0680.0310.0100.0130.0180.0220.0180.0180.0290.0060.3400.2501.000-0.009-0.0520.0780.1050.0520.0640.2460.0000.0000.0000.0030.0000.0080.0040.0000.0030.0000.0000.0000.0010.0000.0000.0000.0000.0000.0051.0000.0001.0001.0000.0001.0000.0000.000
32-terciario_no_universitario-0.0030.0370.017-0.008-0.044-0.014-0.067-0.020-0.070-0.063-0.057-0.061-0.042-0.0290.257-0.0091.0000.5920.3440.2700.2250.2650.1940.0000.0070.0470.0540.0510.0210.0000.0280.0510.0480.0190.0220.0200.0380.0000.0230.0000.0170.0000.0000.0000.3940.0230.0390.0000.0000.000
32-universitarios-0.0280.0060.005-0.045-0.0960.010-0.094-0.008-0.091-0.089-0.059-0.091-0.033-0.0450.246-0.0520.5921.0000.2990.3180.2420.2500.2340.0000.0130.0330.0460.0000.0230.0030.0080.0000.0000.0120.0000.0000.0270.0000.0000.0000.0130.0210.0000.0000.0000.0350.0000.0280.0000.004
33-no_trabajan_pero_estan_buscando_trabajo0.009-0.0080.028-0.0880.036-0.104-0.044-0.061-0.030-0.015-0.021-0.043-0.0020.2030.2150.0780.3440.2991.0000.4060.2610.489-0.0600.0090.0000.0390.0281.0000.0000.0000.0160.0150.0000.0000.0000.0000.0280.0000.0000.0080.0000.0110.0000.0001.0000.0000.0270.0000.0040.000
33-no_trabajan_porque_estudian0.009-0.022-0.003-0.1000.008-0.034-0.053-0.029-0.014-0.054-0.036-0.060-0.0460.3550.1010.1050.2700.3180.4061.0000.4460.333-0.0350.0190.0120.0000.0000.0260.0000.0280.0220.0060.0000.0000.0080.0120.0100.0120.0200.0000.0000.0030.0000.0090.0000.0000.0040.0000.0050.000
33-no_trabajan_y_no_están_buscando-0.0040.0040.006-0.011-0.014-0.048-0.039-0.052-0.024-0.011-0.047-0.0130.0120.2100.0940.0520.2250.2420.2610.4461.0000.235-0.1100.0000.0120.0110.0100.0570.0330.0240.0190.0000.0000.0300.0000.0200.0240.0000.0000.0260.0000.0000.0000.0000.0000.0480.0000.0120.0320.000
33-tienen_planes_trabajar_o_similares0.0310.0980.0310.0620.038-0.109-0.072-0.093-0.045-0.017-0.032-0.017-0.0130.1780.1490.0640.2650.2500.4890.3330.2351.0000.0080.0000.0150.0000.0140.0000.0000.0000.0050.0000.0000.0000.0000.0000.0320.0000.0070.0000.0000.0000.0600.0000.0000.0000.0200.0000.0000.000
33-trabajan0.000-0.0400.0380.1050.0050.040-0.0190.0160.012-0.0060.0070.002-0.0030.3090.3800.2460.1940.234-0.060-0.035-0.1100.0081.0000.0000.0000.0000.0000.0010.0000.0110.0110.0040.0000.0000.0000.0080.0060.0060.0000.0070.0000.0100.0000.0070.0000.0000.0090.0000.0000.000
4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar?0.1110.0270.1101.0000.0000.0080.0000.0000.0000.0530.0000.0000.0010.0070.0010.0000.0000.0000.0090.0190.0000.0000.0001.0000.0340.0300.0320.0280.0240.0070.0230.0550.0310.0140.0450.0720.0310.0310.0250.0340.0210.0000.0000.0190.0540.0240.0130.0790.0110.018
7-¿Qué nivel estás cursando?0.1130.0560.0781.0000.0000.0230.0000.0000.0000.0180.0020.0001.0000.0070.0000.0000.0070.0130.0000.0120.0120.0150.0000.0341.0000.2140.3730.1700.0960.0530.0650.2790.1380.1100.1360.1210.1840.0800.0660.0520.1130.0800.0390.0460.0970.0150.0230.2230.0250.024
8-¿Cuál fue el nivel mas alto que terminaste?0.1050.0650.0681.0000.0170.0180.0230.0280.0280.0181.0000.0000.0180.0220.0000.0000.0470.0330.0390.0000.0110.0000.0000.0300.2141.0000.4300.0910.0590.0670.0610.3430.1550.1260.2850.2330.1670.1310.0810.1250.0730.0640.0790.0470.1800.0290.0350.4290.0660.072
9-¿Terminaste o estás cursando tus estudios en un Colegio?0.1480.0590.0291.0000.0100.0080.0000.0470.0000.0180.0000.0120.0100.0190.0000.0030.0540.0460.0280.0000.0100.0140.0000.0320.3730.4301.0000.2350.0790.1000.0670.2210.2480.0930.2130.1310.0920.1080.0510.0970.0520.0760.0140.0290.2310.0410.0590.2700.0180.045
10-¿El Colegio es… (Privado/Público)0.0760.3010.0681.0000.0000.0291.0001.0000.0000.0090.0001.0001.0000.0951.0000.0000.0510.0001.0000.0260.0570.0000.0010.0280.1700.0910.2351.0000.2380.1640.1510.0640.1860.1080.0110.0200.0960.0080.0450.0660.2240.2260.0000.1350.1320.0740.0000.0220.0610.106
11-Número del Colegio0.0800.0450.0351.0000.0000.0060.0000.0000.0000.0230.0000.0001.0000.0001.0000.0080.0210.0230.0000.0000.0330.0000.0000.0240.0960.0590.0790.2381.0000.0430.0760.1070.1730.0520.0570.0300.0430.0280.0630.0290.0650.0740.0630.0410.5030.0080.0710.0660.0370.031
17-¿Tenés Obra Social y/o Mutual?0.1020.0820.0701.0000.0000.0380.0000.0000.0000.0080.0000.0000.0000.0940.0000.0040.0000.0030.0000.0280.0240.0000.0110.0070.0530.0670.1000.1640.0431.0000.2650.1510.0990.0790.2440.2610.1370.0910.0340.0730.1460.0860.0610.1060.0460.0410.1420.2640.0470.054
18-¿Trabajás actualmente?0.0910.0650.0221.0000.0000.0250.0000.0000.0000.0190.0000.0001.0000.1150.0000.0000.0280.0080.0160.0220.0190.0050.0110.0230.0650.0610.0670.1510.0760.2651.0000.0640.1290.1590.3770.2880.1240.0910.0250.1590.2330.1150.0290.1050.0830.0260.1570.2270.0630.051
19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es:0.0720.0280.0311.0000.0030.0100.0000.0000.0050.0210.0010.0000.0000.0260.0000.0030.0510.0000.0150.0060.0000.0000.0040.0550.2790.3430.2210.0640.1070.1510.0641.0000.9400.0720.0560.0110.0910.0640.0450.0500.1030.0280.0410.0210.0740.0420.0000.5150.0000.057
20-En tu trabajo principal sos...0.0700.0730.0501.0000.0000.0001.0001.0000.0000.0240.0001.0001.0000.0341.0000.0000.0480.0000.0000.0000.0000.0000.0000.0310.1380.1550.2480.1860.1730.0990.1290.9401.0000.1000.2400.2180.0560.1180.0430.0760.0250.0590.0000.0260.6120.0000.0280.1740.0260.073
21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal?0.0330.0400.0181.0000.0070.0101.0001.0000.0220.0100.0001.0001.0000.0171.0000.0000.0190.0120.0000.0000.0300.0000.0000.0140.1100.1260.0930.1080.0520.0790.1590.0720.1001.0000.3260.2020.1090.1920.0460.1750.0370.0540.0460.0300.1690.0080.0160.1370.0200.047
23-¿Tu trabajo principal es?0.0660.0720.0421.0000.0000.0041.0001.0000.0000.0390.0041.0001.0000.0631.0000.0000.0220.0000.0000.0080.0000.0000.0000.0450.1360.2850.2130.0110.0570.2440.3770.0560.2400.3261.0000.4900.2310.1660.0810.1960.0310.0000.0530.0180.0000.0000.0880.3330.0220.073
24-En una semana normal de trabajo ¿cuántas horas trabajás?0.0490.0730.0431.0000.0070.0061.0001.0000.0310.0170.0001.0001.0000.0651.0000.0010.0200.0000.0000.0120.0200.0000.0080.0720.1210.2330.1310.0200.0300.2610.2880.0110.2180.2020.4901.0000.2410.1930.0730.1350.0220.0090.0250.0250.0000.0000.0800.3030.0130.074
25-Tus horarios son…0.0360.0420.0341.0000.0000.0001.0001.0000.0190.0190.0031.0001.0000.0171.0000.0000.0380.0270.0280.0100.0240.0320.0060.0310.1840.1670.0920.0960.0430.1370.1240.0910.0560.1090.2310.2411.0000.1490.0640.1050.0360.0230.0570.0340.0970.0290.0650.1720.0300.025
26-¿Qué momentos del día abarca tu jornada de trabajo?0.0330.0590.0231.0000.0000.0001.0001.0000.0000.0190.0131.0001.0000.0221.0000.0000.0000.0000.0000.0120.0000.0000.0060.0310.0800.1310.1080.0080.0280.0910.0910.0640.1180.1920.1660.1930.1491.0000.1490.2940.0230.0000.0000.0090.1220.0000.0560.1290.0180.044
27-¿Podés cambiar los horarios de trabajo?0.0520.0380.0471.0000.0000.0051.0001.0000.0180.0000.0141.0001.0000.0041.0000.0000.0230.0000.0000.0200.0000.0070.0000.0250.0660.0810.0510.0450.0630.0340.0250.0450.0430.0460.0810.0730.0640.1491.0000.0330.0450.0230.0000.0290.1090.0000.0330.0940.0040.009
28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan)0.0500.0420.0251.0000.0060.0131.0001.0000.0000.0000.0001.0000.0040.0061.0000.0000.0000.0000.0080.0000.0260.0000.0070.0340.0520.1250.0970.0660.0290.0730.1590.0500.0760.1750.1960.1350.1050.2940.0331.0000.0160.0190.0000.0140.0700.0000.0090.0680.0080.021
34-madre0.0610.0270.0291.0000.0110.0110.0001.0000.0000.0100.0010.0000.0000.0370.0190.0000.0170.0130.0000.0000.0000.0000.0000.0210.1130.0730.0520.2240.0650.1460.2330.1030.0250.0370.0310.0220.0360.0230.0450.0161.0000.1970.0580.1400.0410.0360.1280.0470.1090.063
34-¿Tenés conocimientos de otros idiomas?0.1150.0450.0211.0000.0000.0190.0031.0000.0000.0240.0050.0001.0000.0610.0000.0050.0000.0210.0110.0030.0000.0000.0100.0000.0800.0640.0760.2260.0740.0860.1150.0280.0590.0540.0000.0090.0230.0000.0230.0190.1971.0000.0100.0000.0030.0300.1110.0910.1580.128
36-francés0.0470.0310.0551.0001.0000.0001.0001.0000.0000.0001.0001.0001.0000.0401.0001.0000.0000.0000.0000.0000.0000.0600.0000.0000.0390.0790.0140.0000.0630.0610.0290.0410.0000.0460.0530.0250.0570.0000.0000.0000.0580.0101.0000.1720.5660.5140.0500.0640.0860.109
36-inglés0.0620.0280.0191.0000.0000.0091.0001.0000.0000.0000.0000.0001.0000.0320.0000.0000.0000.0000.0000.0090.0000.0000.0070.0190.0460.0470.0290.1350.0410.1060.1050.0210.0260.0300.0180.0250.0340.0090.0290.0140.1400.0000.1721.0000.1670.2170.0490.0320.1220.162
36-otros0.0000.0380.0001.0001.0000.0001.0001.0000.0000.1391.0001.0001.0000.0001.0001.0000.3940.0001.0000.0000.0000.0000.0000.0540.0970.1800.2310.1320.5030.0460.0830.0740.6120.1690.0000.0000.0970.1220.1090.0700.0410.0030.5660.1671.0000.4250.1990.0000.0470.145
36-portugués0.0220.0170.0341.0000.0000.0001.0001.0000.0000.0001.0001.0001.0000.0210.0001.0000.0230.0350.0000.0000.0480.0000.0000.0240.0150.0290.0410.0740.0080.0410.0260.0420.0000.0080.0000.0000.0290.0000.0000.0000.0360.0300.5140.2170.4251.0000.0430.0380.0540.100
37-tu_casa0.1060.1750.0221.0000.0000.0211.0001.0000.0000.0400.0000.0000.0030.0630.0000.0000.0390.0000.0270.0040.0000.0200.0090.0130.0230.0350.0590.0000.0710.1420.1570.0000.0280.0160.0880.0800.0650.0560.0330.0090.1280.1110.0500.0490.1990.0431.0000.3570.0710.065
37-tu_trabajo0.1330.1180.0821.0000.0110.0001.0001.0000.0150.0550.0001.0001.0000.0821.0001.0000.0000.0280.0000.0000.0120.0000.0000.0790.2230.4290.2700.0220.0660.2640.2270.5150.1740.1370.3330.3030.1720.1290.0940.0680.0470.0910.0640.0320.0000.0380.3571.0000.0910.164
38-planilla_de_cálculo0.0600.1660.0121.0000.0000.0071.0001.0000.0000.0080.0000.0001.0000.0280.0050.0000.0000.0000.0040.0050.0320.0000.0000.0110.0250.0660.0180.0610.0370.0470.0630.0000.0260.0200.0220.0130.0300.0180.0040.0080.1090.1580.0860.1220.0470.0540.0710.0911.0000.346
38-procesador_de_texto0.0340.0820.0231.0000.0060.0091.0001.0000.0190.0160.0000.0000.0110.0180.0000.0000.0000.0040.0000.0000.0000.0000.0000.0180.0240.0720.0450.1060.0310.0540.0510.0570.0730.0470.0730.0740.0250.0440.0090.0210.0630.1280.1090.1620.1450.1000.0650.1640.3461.000

Missing values

2023-10-29T11:54:34.964872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-29T11:54:38.241005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-29T11:54:48.383085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

carreracohortesededocumento1-¿Con quien vas a vivir durante el periodo de clases?2-¿Por qué medio te trasladarás hasta la sede donde va a cursar durante el periodo de clases?3-minutos4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar?5-es_gratuita5-me_la_recomendaron5-no_se_toma_examen_de_ingreso5-otras_razones5-por_el_prestigio_de_la_carrera5-por_la_cercanía5-por_su_calidad_académica5-se_dicta_la_carrera_que_prefiero6-medio_o_forma_por_el_cual_conoció_la_universidad7-¿Qué nivel estás cursando?8-¿Cuál fue el nivel mas alto que terminaste?9-¿Terminaste o estás cursando tus estudios en un Colegio?10-¿El Colegio es… (Privado/Público)11-Número del Colegio12-número_del_colegio13-nombre_del_colegio14-partido/depto14-país14-provincia15-Orientación del título de Nivel Medio o Polimodal16-descripción17-¿Tenés Obra Social y/o Mutual?18-¿Trabajás actualmente?19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es:20-En tu trabajo principal sos...21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal?22-¿Tu trabajo principal es?23-¿Tu trabajo principal es?24-En una semana normal de trabajo ¿cuántas horas trabajás?25-Tus horarios son…26-¿Qué momentos del día abarca tu jornada de trabajo?27-¿Podés cambiar los horarios de trabajo?28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan)29-no29-si30-abuelas/os30-amigas/os30-esposa/o_o_pareja30-hermanas/os30-hijas/os30-madre30-otros_(especificar)30-padre30-sobrinas/os30-vivo_sola/o31-especificar32-medio_o_polimodal32-primario_o_egb32-terciario_no_universitario32-universitarios33-no_trabajan_pero_estan_buscando_trabajo33-no_trabajan_porque_estudian33-no_trabajan_y_no_están_buscando33-son_jubilados_o_pensionados33-tienen_planes_trabajar_o_similares33-trabajan34-madre34-padre34-¿Tenés conocimientos de otros idiomas?36-especificar36-francés36-inglés36-otros36-portugués37-especificar37-otros37-tu_casa37-tu_trabajo38-especificar38-navegación_por_internet38-otros38-planilla_de_cálculo38-procesador_de_texto
051.0201311.010625103.01-mi_grupo_familiar2-en_colectivo_de_corta_distancia40.04-nadaNaNNaNNaN1.0NaNNaNNaNNaNNaN7-noNaN9-medio/polimodal10-público11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)NaNcens nro.2NaNNaNNaNperito comercial (especialidad costos)costos17-si18-si19-si20-parcial21-obrero_o_empleado22-educación23-formal24-permanente25-de_25_a_48_horasNaN27-mañana28-sólo_algunas_vecesNaNNaNNaNNaN30-esposa/o_o_parejaNaN30-hijas/osNaNNaNNaNNaNNaN3.03.0NaNNaNNaNNaNNaNNaNNaNNaNNaNPrimario CompletoPrimario Completo35-siNaNNaNIntermedioNaNNaNNaNNaNSiSiNaNIntermedioNaNNaNNaN
13.020132.011939636.01-mi_grupo_familiar2-en_colectivo_de_media_distancia90.04-mucho1.0NaNNaNNaN4.02.03.0NaNNaN7-noNaN9-medio/polimodal10-público11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)NaNLA RECONQUISTATIGREARGENTINABUENOS AIRES15-bachillerGESTION Y ADMINISTRACION17-no18-si19-si20-parcial21-trabajador_familiarNIÑERA23-informal24-temporario25-de_25_a_48_horas26-fijos27-mañana28-nuncaNaNNaNNaNNaNNaNNaN30-hijas/osNaNNaNNaNNaNNaN2.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNMedio o Polimodal IncompletoPrimario Incompleto35-noNaNNaNNaNNaNNaNNaNNaNSiNaNNaNAvanzadoNaNBuenoBueno
25.020132.014077669.01-mi_grupo_familiar2-en_colectivo_de_corta_distancia30.04-mucho1.01.01.0NaN1.01.01.01.0NaN7-si8-medio/polimodal9-primario10-público11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)NaNdorregohurlinghamargentinabuenos aires15-bachillergestion y economia17-si18-no19-si20-parcial21-obrero_o_empleado22-comercio23-informal24-permanente25-de_11_a_24_horas26-fijos27-mañana28-nuncaNaNNaNNaNNaNNaNNaN30-hijas/osNaNNaNNaNNaNNaN3.02.03.02.0NaNNaN1.00.000.02.0Primario IncompletoPrimario Incompleto35-siNaNNaNBuenoNaNNaNNaNNaNSiNaNNaNBuenoNaNBuenoBueno
33.020132.014386809.01-mi_grupo_familiar2-en_auto20.04-mucho6.03.07.0NaN2.05.01.04.0NaN7-noNaN9-terciario10-público11-censNaNSindicato de Luz y FuerzaMoronArgentinaBs.As15-técnicoAdministracion de PYME17-si18-si19-si20-parcial21-obrero_o_empleado22-comercio23-formal24-permanente25-de_25_a_48_horas26-fijos27-mañana28-sólo_algunas_vecesNaNNaNNaNNaNNaN30-hermanas/os30-hijas/os30-madreNaNNaNNaNNaN2.01.0NaNNaNNaNNaNNaNNaNNaNNaN1.0Primario CompletoPrimario Completo35-noNaNNaNNaNNaNNaNNaNNaNSiSiNaNBuenoNaNBuenoBueno
45.020133.017055269.01-mi_grupo_familiar2-en_colectivo_de_corta_distancia40.04-muchoNaN3.0NaNNaNNaN1.02.0NaNNaN7-noNaN9-medio/polimodal10-público11-escuela_de_educación_media_(polimodal,_ex-comercial,_ex-normal_o_nacional)NaNEscuela de Eduación Media 14 de SetiembreExaltacion de la CruzArgentinaBuenos Aires15-bachillerBachiller con Orientación en Computación17-no18-si19-si20-parcial21-obrero_o_empleado22-servicios_públicos23-formal24-permanente25-más_de_48_horas26-fijos27-mañana28-nuncaNaNNaNNaNNaN30-esposa/o_o_parejaNaN30-hijas/osNaNNaNNaNNaNNaN4.01.01.0NaN1.0NaNNaNNaNNaNNaN1.0Primario CompletoPrimario Incompleto35-noNaNNaNNaNNaNNaNNaNNaNSiNaNNaNIntermedioIntermedioIntermedioIntermedio
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carreracohortesededocumento1-¿Con quien vas a vivir durante el periodo de clases?2-¿Por qué medio te trasladarás hasta la sede donde va a cursar durante el periodo de clases?3-minutos4-En la elección de la UNLu, ¿Cuánto influyó la cercanía de la sede donde vas a cursar?5-es_gratuita5-me_la_recomendaron5-no_se_toma_examen_de_ingreso5-otras_razones5-por_el_prestigio_de_la_carrera5-por_la_cercanía5-por_su_calidad_académica5-se_dicta_la_carrera_que_prefiero6-medio_o_forma_por_el_cual_conoció_la_universidad7-¿Qué nivel estás cursando?8-¿Cuál fue el nivel mas alto que terminaste?9-¿Terminaste o estás cursando tus estudios en un Colegio?10-¿El Colegio es… (Privado/Público)11-Número del Colegio12-número_del_colegio17-¿Tenés Obra Social y/o Mutual?18-¿Trabajás actualmente?19-La relación que hay entre tu trabajo y la Carrera que elegiste cursar es:20-En tu trabajo principal sos...21-¿En cuál de las siguientes ramas de la actividad ubicarías a tu trabajo principal?23-¿Tu trabajo principal es?24-En una semana normal de trabajo ¿cuántas horas trabajás?25-Tus horarios son…26-¿Qué momentos del día abarca tu jornada de trabajo?27-¿Podés cambiar los horarios de trabajo?28-¿Estás en este momento buscando trabajo? (Solo para quienes no trabajan)29-no29-si30-abuelas/os30-amigas/os30-esposa/o_o_pareja30-hermanas/os30-hijas/os30-madre30-padre30-sobrinas/os30-vivo_sola/o31-especificar32-medio_o_polimodal32-primario_o_egb32-terciario_no_universitario32-universitarios33-no_trabajan_pero_estan_buscando_trabajo33-no_trabajan_porque_estudian33-no_trabajan_y_no_están_buscando33-tienen_planes_trabajar_o_similares33-trabajan34-madre34-¿Tenés conocimientos de otros idiomas?36-especificar36-francés36-inglés36-otros36-portugués37-otros37-tu_casa37-tu_trabajo38-especificar38-navegación_por_internet38-otros38-planilla_de_cálculo38-procesador_de_texto# duplicates
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